20100301_JurnalOKIVo..

Jurnal Otomasi, Kontrol & 

Instrumentasi 

Journal of Automation, Control and 

Instrumentation 

Volume 1, No.2, Tahun 2009 

Diterbitkan oleh/Published by : 

Masyarakat Otomasi, Kontrol dan Instrumentasi 

Society of Automation, Control and Instrumentation 

2 

ISSN : 2085‐2517


Instrumentasi 




Masyarakat Otomasi, Kontrol dan Instrumentasi 

Alamat : Litbang (ex.PAU) Lt.8 Jl. Ganesa 10 Bandung 40132, Indonesia 

Tel. +62-22-2514452 Tel / Fax. +62-22-2534285. 

Email : jurnal_oki@instrument.itb.ac.id

Tim Editor 

(Board Editor) 

[Ketua/Chairman] 

Deddy Kurniadi 

(Institut Teknologi Bandung, Indonesia) 

[Anggota/Member] 

Bambang Lelono 

(Institut Teknologi Sepuluh November Surabaya, Indonesia) 

Bolo Dwiartomo 

(Politeknik Manufaktur Bandung, Indonesia) 

Estiyanti Ekawati 


Mitra Djamal 


Parsaulian Siregar 


Riza Muhida 

(International Islamic University Malaysia, Malaysia) 

Satriyo Nugroho 

(PT. Petrokimia Gresik, Indonesia) 

Sudarto Ramli 

(PT.Yokogawa Indonesia) 

Suprijadi 


Togar MP Manurung 

(PT. Pertamina, Indonesia) 

Titon Dutono 

(Politeknik Elektronika Negeri Surabaya, Indonesia) 

Son Kuswadi 

(Institut Teknologi Surabaya) 

Waskita Indrasutanta 

(PT. Wifgasindo Dinamika Instrument, Indonesia) 

Yudi Samyudia 

(Curtin University of Serawak, Malaysia) 

ii

Kata Pengantar 

Dewasa ini penelitian-penelitian dalam bidang otomasi, kontrol dan instrumentasi 

telah mendorong perkembangan yang sangat pesat pada teknologi industri 

manufaktur, energi, makanan, kesehatan, transportasi, militer dan sebagainya. 

Teknologi tersebut mempunyai peranan yang sangat penting untuk meningkatkan 

kualitas baik produk maupun proses di industri serta untuk menjaga kelestarian 

lingkungan dan kesehatan, yang pada akhirnya sangat menentukan daya saing 

suatu industri maupun daya saing bangsa. 

Dengan maksud untuk lebih mengenalkan hasil-hasil penelitian dalam bidang 

otomasi, kontrol dan instrumentasi ke masyarakat serta memberikan sarana untuk 

bertukar informasi bagi para peneliti, praktisi dan pengguna teknologi tersebut, 

kami dari Masyarakat Otomasi, Kontrol dan Instrumentasi (Society of Automation, 

Control and Instrumentation) menerbitkan jurnal ilmiah yang dikhususkan pada 

bidang yang disebutkan di atas. 

Pada penerbitan jurnal kedua ini, makalah-makalah ilmiah yang dipublikasikan 

merupakan hasil kegiatan riset di lingkungan Institut Teknologi Bandung, 

perguruan tinggi baik dalam dan luar negeri. Kami sebagai pengurus jurnal telah 

mengundang para ahli/pakar baik sebagai peneliti dari berbagai universitas 

maupun praktisi dari industri sebagai anggota tim editor. Kami juga mengundang 

para peneliti, praktisi dan pengguna teknologi di bidang yang dimaksud untuk 

menerbitkan makalah hasil penelitian yang dilakukan di jurnal ini. 

Jurnal ini akan didistribusikan ke berbagai universitas, lembaga penelitian, industri 

serta individu terkait. Kami berharap dalam beberapa tahun ke depan, jurnal ini 

sudah dapat diajukan untuk diakreditasi. Dengan demikian, diharapkan misi jurnal 

ini sebagai bagian dalam pengembangan dan diseminasi ilmu dan teknologi 

otomasi, kontrol dan instrumentasi dapat tercapai. 

Terakhir, kami sampaikan terima kasih yang sebesarnya kepada para penulis yang 

telah mengajukan makalahnya untuk diterbitkan di jurnal ini dan kepada para 

pengurus Masyarakat Otomasi, Kontrol dan Instrumentasi yang telah menginisiasi 

jurnal ini. Tidak lupa kami sampaikan juga terima kasih kepada para ahli/pakar 

atas kesediaanya menjadi editor jurnal ini. 

iii

Preface 

Automation, control, and instrumentation are the key to the advancement of 

science, engineering, and technology. Progress in these fields have become 

thoroughly integrated into all aspects of technology in our modern lives - from 

agriculture, health to national security. These have a vital role to improve the quality 

of both industrial products and processes and to preserve the environment and 

health of which ultimately determines the competitiveness of an industry and a 

nation. Therefore, the Society of Automation, Control and Instrumentation has 

published this journal as a part of our responsibility to introduce the results of 

researches to the community and as a means to exchange information between 

researchers and users of this technology. 

In this second edition, published papers are the results of research activities within 

the Bandung Institute of Technology and other universities. We have invited 

researchers and practitioners from industries as a member of the editorial team. 

We also invite researchers and practitioners to publish their research in this journal. 

This journal will be distributed to various universities, research institutions, industry 

participants and contributing authors. We hope in the next few years, this journal 

has been filed for accredited. Thus, the mission of this journal as a part of the 

development and dissemination of science and technology of automation, control 

and instrumentation can be achieved. 

Finally, we thank to the authors who have submitted papers in this journal and to 

the Society of Automation, Control & Instrumentation who has initiated this journal. 

Our gratitude also goes to the researchers and practitioners on the willingness to be 

the editor of this journal. 

iv

Daftar Isi/List of Content 

Tim Editor/Editor Board ii 

Kata Pengantar/Preface iii 

Daftar isi/List of Content iv 

1 Development of Circularly Polarized Synthetic Aperture Radar 

Sensor Mounted on Unmanned Aerial Vehicle 

M. Baharuddin, P.R. Akbar, J.T.S. Sumantyo, H. Kuze 

2 Electric Traction Motor Drive Modelling for Electric Karting 

Application Using Matlab / Simulink Software 

D. Istardi 

3 Feasibility Study of Solar Power Massive Usage in Indonesia : 

Yield versus Cost Effective 

M.A. Setiawan 

4 Modelling and Designing The Model Predictive Control System of 

Turbine Angular Speed at Hydropowerplant UBP Saguling PT 

Indonesia Power 

R.K.A. Kusumah, E. Joelianto, E. Ekawati 

5 Performance Analysis of Finger Flexor and Finger Extensor 

Muscles on Wall Climbing Athletes trough Electromyography 

Measurement, Handgrip Strength, Handgrip Endurance and 

Lactate Acid 

H. Susanti, Suprijanto, F. Idealistina, T. Apriantono 

6 Study on Voltage Controller of Self-Excited Induction Generator 

Using Controlled Shunt Capacitor, SVC Magnetic Energy Recovery 

Switch 

F.D. Wijaya, T. Isobe, R. Shimada 

v 

1 

7 

21 

29 

41 

49

J.Oto.Ktrl.Inst (J.Auto.Ctrl.Inst) Vol 1 (2), 2009 ISSN : 2085-2517 

Development of Circularly Polarized Synthetic Aperture Radar 

Sensor Mounted on Unmanned Aerial Vehicle 

M. Baharuddin, P.R. Akbar, J.T.S.Sumantyo, and H. Kuze 

Microwave Remote Sensing Laboratory, Center for Environmental Remote Sensing, Chiba 

University, 1-33, Yayoi, Inage, Chiba 263-8522 Japan, merna5@graduate.chiba-u.jp 

Abstract 

This paper describes the development of a circularly polarized microstrip antenna, as a part of the 

Circularly Polarized Synthetic Aperture Radar (CP-SAR) sensor which is currently under developed at 

the Microwave Remote Sensing Laboratory (MRSL) in Chiba University. CP-SAR is a new type of sensor 

developed for the purpose of remote sensing. With this sensor, lower-noise data/image will be 

obtained due to the absence of depolarization problems from propagation encounter in linearly 

polarized synthetic aperture radar. As well the data/images obtained will be investigated as the Axial 

Ratio Image (ARI), which is a new data that is expected to reveal unique various backscattering 

characteristics. The sensor will be mounted on an Unmanned Aerial Vehicle (UAV) which will be aimed 

for fundamental research and applications. The microstrip antenna works in the frequency of 1.27 

GHz (L-Band). The microstrip antenna utilized the proximity-coupled method of feeding. Initially, the 

optimization process of the single patch antenna design involving modifying the microstrip line feed to 

yield a high gain (above 5 dBi) and low return loss (below -10 dB). A minimum of 10 MHz bandwidth is 

targeted at below 3 dB of Axial Ratio for the circularly polarized antenna. A planar array from the 

single patch is formed next. Consideration for the array design is the beam radiation pattern in the 

azimuth and elevation plane which is specified based on the electrical and mechanical constraints of 

the UAV CP-SAR system. This research will contribute in the field of radar for remote sensing 

technology. The potential application is for landcover, disaster monitoring, snow cover, and 

oceanography mapping. Especially for Indonesia which is the largest archipelago country in the world, 

the need for surface mapping and monitoring is demanding. 

Keywords: synthetic aperture radar, circular polarization, microstrip antenna 

1 Introduction 

A circularly-polarized Synthetic Aperture Radar (CP-SAR) to be launched onboard a microsatellite 

is currently developed in the Microwave Remote Sensing Laboratory (MRSL) of the 

Center for Environmental Remote Sensing, Chiba University. SAR is a multipurpose sensor 

that can be operated in all-weather and day-night time. As part of the project, an airborne 

CP-SAR development is also undertaken in order to gain sufficient knowledge of CP-SAR 

sensor systems. An L-band CP-SAR system will be designed for operation onboard an 

unmanned aerial vehicle (UAV). 

Historically, synthetic aperture radars (SAR) have used linearly polarized (LP) antenna 

systems. However, there are limitations due to the propagation phenomenon namely the 

variation of geometric differences between earth and the radar, the occurrence of a phase 

shift as a result of radio wave strike the smooth reflective surface, etc. These phenomenon 

leads to a backscatter variation, random redistribution of returned signal-energy and in the 

end the formed image would encounter a spatially variant blurring and defocusing as well as 

ambiguous identification of different low-backscatter features in a scene. 

1


As compared with the conventional linear polarization SAR, most of the above-mentioned 

effects can be alleviated through the use of CP-SAR. Thus, a CP-SAR sensor would provide a 

greater amount of information about scenes and targets being imaged than a linear SAR 

sensor. The present work focuses on the design of an L-band CP-SAR antenna. We consider 

the SAR system requirements to achieve an excellent performance of the overall CP-SAR 

system, including optimization of the single element patch and array designing. 

2 Circularly Polarized SAR Antenna Requirements 

Table 1. Specification of CP-SAR onboard Unmanned Aerial Vehicle 

Parameter Specification 

Frequency f 1.27 GHz (L band) 

Chirp bandwidth 10 MHz 

Polarization Transmitter : RHCP 

Receiver : RHCP + LHCP 

Gain G > 20 dBic 

Axial Ratio AR < 3 dB (main beam) 

Antenna size 1.75 m (azimuth) 

0.5 m (range) 

Beam width 8º (azimuth) 

25º (range) 

Altitude range 3,000 - 10,000 m 

The capability of a SAR antenna can be described by its sensitivity, spatial resolution in 

range and azimuth directions, image quality, ambiguities, and swath coverage [1]. Table 1 

shows the specifications and targets desired for the present CP-SAR system, which in turn 

influence the specification of the L-Band CP-SAR antenna. 

The operation frequency of 1.27 GHz (L-band) has been chosen, since its relatively longer 

wavelength ensures better penetration through vegetation canopies. The drawback 

associated with this choice, however, is the relatively large dimension of microstrip 

elements. The requirements for the range resolution (15 m) determine the antenna 

bandwidth of 10 MHz, or less than 1% of the operation frequency of 1.27 GHz. This 

bandwidth requirement must be compatible with a low axial ratio (AR) (below 3 dB) for 

ensuring transmitting/receiving circularly-polarized waves. To satisfy the matching of input 

impedance, the return loss must be smaller than 10 dB in this bandwidth range. 

3 CP-SAR Antenna Concept Analysis 

The primary considerations in the design and fabrication process are low cost, light weight 

and ease of manufacturing. The CP-SAR antenna is conceived in the way that every single 

element microstrip patch is a circularly polarized antenna. Feed network will be 

implemented in different layer substrate as the feeding method is proximity coupled. 

2


Figure 1. Configuration of a CP-SAR antenna array consisting of microstrip elements. 

The primary considerations in the design and subsequent fabrication processes are low 

cost, light weight and ease of manufacturing. The CP-SAR antenna consists of an array of 

single antenna elements, each being a microstrip antenna for circular polarization. The 

single element patches which have been optimized are then spatially arranged to form a 

planar array (see Figure 1 for illustration). The planar array configuration is widely employed 

in radar systems where a narrow pencil beam is needed [2]. The beam pattern for optimum 

ground mapping function is cosecant-squared beam in the elevation plane (E-plane) which 

can correct the range gain variation and pencil beam in the azimuth plane (H-plane) [3]. The 

antenna side lobe levels in the azimuth plane must be suppressed in order to avoid the 

azimuth ambiguity. To deal with reflection, the antenna side lobes and back lobes also must 

be suppressed. A better control of the beam shape and position in space can be achieved 

by correctly arranging the elements along a rectangular grid to form a planar array. The 

antenna gain is mostly determined by the aperture size and inter-element separation. 

To maximize the array performance, certain characteristics of feed networks have to be 

taken into account. These are the conductor and dielectric losses, surface wave loss, and 

spurious radiation due to discontinuities such as bends, junctions, and transitions [2]. The 

loss due to the coupling of the adjacent element have to be considered, therefore isolation 

between adjacent elements must be higher than 20 dB. The spacing between elements is 

measured as the distance between the midpoints of each element. A maximum directivity 

will occur for approximately spacing between elements in the range of 0.8 – 0.9 times the 

free space wavelength [4]. 

4 Analysis and Design of Radiating Elements 

The configuration of the radiating element together with the microstrip line feed and ground 

plane is shown in Figure 2(a), where important parameters are labelled. Side view is 

depicted in Figure 2(b). The equilateral triangular radiator will generate a left-handed 

circular polarization (LHCP) by employing the dual feed method as shown in Figure 2(a). In 

order to generate a 90 o phase delay on one of the two modes, the line feed on the left side 

is approximately λ/4 longer than the other. 

Simulations with a finite ground plane model have been undertaken to optimize the size 

parameters using a full wave analysis tool (IE3D Zeland software) based on the method of 

moment (MoM) algorithm. The dimensions of the radiator, microstrip feed line and the 

ground plane for the equilateral triangular patch are a = 102.75, w = 6.8, ld = 21.5, le = 27, 

ld1 = 6.9, lc = 9.2, ls = 10.1, lm = 3.9, lst = 21.5, ws = 10.2, la = 146.1, and lr =163.1 in units 

3


of mm. The geometry model is implemented on two substrates, each with thickness t = 1.6 

mm, with the conductor thickness tc ≈ 0.035 mm, relative permittivity εr = 2.17 and loss tan 

δ (dissipation factor) 0.0005. The equilateral triangular microstrip antenna model has been 

fabricated to verify the simulation results. The reflection coefficient and input impedance 

were measured with a RF Vector Network Analyzer (Agilent, E5062A, ENA-L). The antenna 

gain, AR, and radiation patterns were measured inside the anechoic chamber of MRSL, 

having a dimension of 4×8.5×2.4 m. 

The experimental results are shown in Figures 3 – 5 in comparison with the simulation. 

Figure 3 shows the S-parameter which indicate an impedance bandwidth of more than 15 

MHz. 

In Figure 4, it can be seen that whereas the gain of the antenna is simulated to be 7.04 

dBic at 1.27 GHz, the experimental result shows a smaller value by about 0.6 dB. This 

difference may be ascribed to the fabrication imperfections (such as inaccuracy in the 

milling and etching processes and connector soldering) and the substrate loss. The 3-dB AR 

bandwidth of the simulation is 7.2 MHz and from observation it is 7.4 MHz which is still 

narrower than the target specification (10 MHz). To improve this situation, the next work will 

consider the technique to extend the 3-dB AR bandwidth. 

Figure 5 shows the radiation pattern in terms of gain an azimuth angle Az = 0 o (x-z plane) at 

the frequency of f = 1.27 GHz. A difference of around 0.7 dB is seen between simulated 

model and the measured antenna on the gain radiation pattern. There are some differences 

between the simulated and measured pattern of the antenna. This may be due to the 

imbalance in current distribution affected by the configuration of the antenna (such as holes 

and plastic screws in substrate) and the measurement system. 

Figure 2. Configuration of equilateral triangular patch antenna with proximity coupled feed; 

(a) top view and (b) side view. 

4


S 11 - Reflection Coefficient (dB) 

- Gain (dBic) 

G 

0 

-10 

-20 

-30 

Simulation 

Measurement 

-40 

1.25 1.26 1.27 

Frequency (GHz) 

1.28 1.29 

Figure 3. Reflection coefficient vs. frequency 

Gain (Simulation) AR (Simulation) 

8 

Gain (Measurement) AR (Measurement) 

8 

7 

7 

6 

6 

5 

5 

4 

4 

3 

3 

2 

2 

1 

1 

0 

1.25 1.26 1.27 

Frequency (GHz) 

1.28 

0 

1.29 

- Gain (dBic) 

G 

Figure 4. Gain and AR vs. frequency at θ (theta) angle = 0o 

8 

7 

6 

5 

4 

3 

2 

1 

0 

...... 

Simulation 

Measurement 

← = 180 

θangle (degrees) 

o 

90 60 30 0 30 60 90 

= 0 o Az Az → 

Figure 5. Gain vs. theta angle (radiation pattern) in the theta plane (Az = 0o and 180o) (x – z plane) at 

f = 1.27 GHz. 

5 

AR - Axial Ratio (dB)


5 Conclusion 

A circularly-polarized antenna has been developed for implementing antenna for circularlypolarized 

synthetic aperture radar (CP-SAR) sensor operated in L-band. The design and 

optimization process was carried out using a MoM analysis software. The model was 

actually fabricated and measured in MRSL. Although the AR bandwidth is slightly smaller 

than the requirement for an airborne CP-SAR system, the present work has indicated that 

the goals can be met through a precise adjustment in the design and fabrication process in 

the near future. 

6 References 

[1] Pokuls, R. , Uher, J. , and Pozar, D.M., September 1998. Dual-Frequency and Dual 

Polarization Microstrip Antennas for SAR Applications, IEEE Trans. Antenna 

Propagation, volume 46, no. 9, pp 1289-1296. 

[2] Garg, R. , Bhartia P. , Bahl I. , and Ittipiboon, A., 2001. Microstrip Antenna Design 

Handbook, Artech House, pp 720, 737. 

[3] Vetharatnam, G. Kuan, C.B, and Teik C.H., Microstrip Antenna for Airbone SAR 

Application 

http://www.remotesensing.gov.my/images/default/publication_3rdmicrowave/3rdmic 

rowave_paper5.pdf 

[4] Levine, E., Malamud, G., Shtrikman, S., and Treves, D., April 1989. A Study of 

Microstrip Array Antennas with the Feed Network, IEEE Trans. Antenna Propagation, 

volume 37, no. 4, pp 426-434. 

7 Acknowledgement 

The authors would like to thank Victor Wissan, Basari, Fauzan, Ilham A. and Zhang Jia-Yi for 

assisting in the antenna fabrication and measurement; the Japan Society for the Promotion 

of Science (JSPS) for Grant-in-Aid for Scientific Research - Young Scientist (A) (No. 

19686025); Venture Business Laboratory - Chiba University for Project 10th Research 

Grant; National Institute of Information and Communication Technology (NICT) for 

International Research Collaboration Research Grant 2008, and other research grants that 

have supported this research. 

6


Abstract 

Electric Traction Motor Drive Modeling for Electric Karting 

Application Using Matlab®/Simulink® Software 

D. Istardi 

Batam Polytechnics 

Parkway st. Batam Centre, Batam Indonesia 

E-mail: istardi@polibatam.ac.id 

An electric traction motor drive for an electric karting application was modeled for efficiency studies 

and simulated using the MATLAB ®/Simulink ® software. The model includes models of a battery, power 

electronic converter, electric motor, and vehicles dynamic of go-karts to a typical 48 seconds track 

driving schedule. The losses of each component of the electric traction motor drive were modeled and 

simulated over the entire speed range. In the battery was also calculated the state of charge (SOC) of 

the battery over the driving cycle. The regenerative braking energy captured was also considered in the 

simulation. Finally, the overall electric traction motor drive system efficiency and energy consumed 

were estimated based on the individual model based efficiency and energy consumed analysis. 

Keywords : Index Terms—Battery, electric go-kart, efficiency maps, loss modeling, and regenerative 

braking. 


The first electric vehicle was made in the 1830s and was popular for almost a century 

[1],[2],[3]. However, since 1933 the numbers of electric vehicles have decreased due to the 

improvements of the internal combustion engine (ICE) that has become better and cheaper. 

Nowadays, environmental considerations, energy costs, and improvements in control and 

battery technology have inspired an increasing amount of research and development of 

electric vehicles [3]. 

One of the developments in the electric vehicle is research on electric kart racing or karting. 

Research in this area is interesting due to their characteristics that were different compare 

to the normal electric go-kart such as energy consumption and acceleration. In electric 

karting, the energy is higher than in the electric go-kart. The drive cycle that was used in the 

electric karting have a high acceleration and deceleration. In this paper, the electric karting 

that would be used as an input for simulation is the ICE karting for children (8-12 years old). 

Therefore, the research is focus on energy consumption and losses of an electric small 

karting. 

Electric karting is a variant of an open-wheel motor sport, with small four-wheeled vehicles 

called karts. These karts are simple and usually raced on a scaled-down track. Since the 

electric kart engine is powered by an electric motor instead of an internal combustion 

engine and the motor is operated using the power stored in batteries [4], its engine has 

many advantages over the ICE. It is pollution-free, has higher energy conversion efficiency 

and less vibration, requires low maintenance, its speed is easy to control and it can use the 

energy from regenerative braking [1], [3]. An electric karting race was first started in 1989 

in Italy [5] and is currently getting popular in the United States and Europe due to 

7


improvements in control and battery technology. 

The components of electric karting are chassis made of a steel tube, a propulsion system 

that includes an electric motor that drives the wheels, a power electronic converter that 

regulates the energy flow to the motor and a transmission system, a battery that provides 

energy, and a control unit that ensures a proper operation of the power electronic converter 

[3],[6], [7]. 

The paper is organized as follows: In the next section, a brief review of electric traction drive 

system components and simulation of each component are presented. In Section III, a 

description of the ICE karting drive cycle is given. Section IV and V presents results of 

simulation using MATLAB®/Simulink® software and discussion of the results. Finally, the 

conclusions are made in section VI. 

2 Electric Traction Drive System 

A simple electric traction drive system consists of a drive system (transmission, electric 

motor, and power electronics) and energy storage (battery) [6] as seen in Fig. 1. Examples of 

some papers that describe the modeling of an electric traction drive system using MATLAB® 

or other software [7]-[10]. 

Within the system, energy is stored in the battery. A power electronic converter connects the 

battery to an electric motor. The voltage and current output of the battery are maintained to 

match the ratings of the electric motor. The electric motor converts electrical energy 

supplied by the battery into mechanical energy. A transmission transforms the mechanical 

energy into a linear motion. Speed and torque are adjusted using the gear box. The gear can 

transmit the rotational force at different speeds, torque, and directions. A controller and 

energy management control the speed and direction of the electric karting, and optimize the 

energy conversion from the battery to the transmission. The battery can be charged from the 

line power and also from regenerative braking energy. 

Figure 1. Block diagram of drive system for electric karting. 

In order to calculate the efficiency of the electric karting, it is essential to understand how 

the losses of each electric drive system component change with speed. 

8


2.1 Vehicle Dynamic Model 

Mechanical energy provided by the electric traction drive system is used to drive the wheels 

of the electric karting. The supplied energy must be large enough to overcome the “traction 

resistance” (Ft), i.e. the sum of rolling resistance (Frr), aerodynamic drag (Fad), climbing 

resistance and acceleration force (Faf) [8], [11]. Rolling resistance is a deformation process 

mechanism which occurs at the contact patch between the tires and road surface. 

Aerodynamic drag is the viscous resistance of air upon the vehicle. In this paper, the race 

track is assumed to be flat, thus the climbing resistance is neglected. Those forces can be 

calculated using: 

F 

t 

= F 

ad 

+ F 

rr 

+ F 

cr 

+ F 

af 

(1) 

F 

ad 

= 

1 

2 

δ C 

ad 

Av 

2 

a 

(2) 

F 

rr 

= C 

rr 

mg 

(3) 

F 

af 

= ma 

(4) 

Where δ : front surface area of vehicle [m2] 

Cad : coefficient aerodynamic drag 

va : relative vehicle speed with respect to air [m/s] 

Crr : coefficient rolling resistance 

m : total vehicle mass, include the driver [kg] 

g : gravitational acceleration [m/s2] 

a : acceleration [m/s2] 

Calculation of the torque generated by the electric motor is based on energy considerations 

in terms of inertias (J), load acceleration, coupling ratio (B), and the load torque (TL) or force 

as shown below: 

d ω 

T 

r 

r 

= J + B ω 

dt r 

+ 

The losses in this model are neglected. The parameters of vehicle dynamics for a small 

electric karting can be seen in Table 1. 

2.2 Electric Motor Losses 

9 

T 

L 

Table 1 . Vehicle dynamic parameter for small go kart 

Total mass 110 kg 

Rolling resistance coeff. 0.03 

Drag coefficient 0.6 

Air density 1.202 kg/m3 Vehicle cross section 0.5 m2 Driving wheel radius 0.14 m 

The losses in the electric motor can be divided into 4 components [12]: copper losses, core 

losses, mechanical losses and stray losses. In this paper, only copper and iron losses will be 

used in simulation and analysis. Mechanical and stray losses are disregarded. The electric 

(5)


motor that used in this paper is induction motor due to its simplicity, minimum maintenance 

requirement, and low costs [13]-[15]. Per-phase equivalent circuit of the induction motor at 

steady state is shown in Figure 2 [15]-[18]. 

Figure 2: Equivalent circuit of induction motor 

From the equivalent circuit in Figure 2 and power flow in induction motor, the rotor current at 

rated condition can be calculated by 

I 

r 

Where Td : developed torque of electric motor [T] 

s : slip [%] 

p : pole pairs 

ωs : angular speed of stator [rad/sec] 

Using the current divider laws, the stator current is calculated by: 

Then the air gaps voltage can be expressed as 

I 

s 

2 T 

d 

ω 

s 

s 

= (6) 

3 pR 

r 

R R 

2 

2 

⎛ m r 

R 

ω 

2 ⎞ ⎡ ⎛ r ⎞⎤ 

⎜ L L ⎟ 

s r m ⎢ω 

⎜ 

s 

R 

m 

L 

m 

L 

m 

R 

m 

L ⎟ 

⎜ 

− 

s 

⎟ 

+ 

⎜ 

+ + 

s r ⎟⎥ 

⎝ 

⎠ ⎢⎣ 

⎝ 

⎠⎥⎦ 

= I 

(7) 

ω L R 

r 

s m m 

s ω T ⎛ 

⎞ 

⎜ R 

2 

⎟ 

V = 

s d r 

⎜ + L 

2 

g 

ω 

3 R 

⎟ 

⎜ 2 s r 

r 

⎟ 

⎝ 

s 

⎠ 

The total rated losses of the motor can be obtained as 

P 

loss 

= 

⎡ 

3 ⎢ R I 

2 

⎢ s s 

⎢⎣ 

+ R I 

2 

r r 

+ 

V 

2 

g 

R 

m 

⎤ 

⎥ 

⎥ 

⎥⎦ 

(9) 

The induction motor can operate above the rated speed by using frequency or voltage 

control variations because of its rugged mechanical construction [10], [18]-[21]. The torque 

and power capabilities as a function of rotor speed can be seen in Figure 3. In the below 

rated speed area, the flux in the air gap is kept constant by controlling Vs/f and the value of 

10 

(8)


the slip is small. Then the electric motor can produce torque until up to its rated torque and 

as a result, the rotor current may be assumed to be proportional to the torque. 

Figure 3. Induction motor characteristic and capabilities 

To increase the motor speed above the rated speed, the stator voltage is kept at the rated 

voltage and the stator frequency is increased to a value above the rated frequency. So, the 

Vs/f is reduced and the flux is also reduced by the ratio of the instantaneous operating 

speed to the rated speed. The rating of the motor can be seen in Table 2. 

Table 2. Induction motor rating 

Power 6000 W 

Voltage 3 x 27 V 

Frequency 100 Hz 

Speed 2850 rpm 

Current 168 A 

Weight 19.2 kg 

From this rating of the motor, efficiency map can be calculated and the result can be seen in 

Figure 4. 

11


Figure 4. Efficiency maps for the used induction motor 

2.3 Power Electronic Converter 

The power electronic converter used in this simulation is a standard six-switch three-phase 

bridge inverter. The aim of this component is to provide appropriate mean values of 

parameters commonly used in electric motor. The power switching device used in this paper 

is MOSFETs (Metal Oxide Semiconductor Field Effect Transistors) and anti-parallel power 

diodes. The controller pulse used in this simulation is a three-phase pulse-width modulation 

(PWM). The main losses in the converter are the conduction losses (Pcond) and switching 

losses (Psw) for each switching component [10],[19]. Switching process of power electronic 

components depends on the load and switching strategies [19],[21]-[25]. The losses for the 

MOSFET are 

P 

Q 

where M : Modulation index (0


The parameters of this component can be found in Table 3. 

Table 3. Power electronic converter parameter 

Power MOSFET MTD3055VL 

Strain drain to source on-resistance 0.012 ohm 

Rise time 85e-9 seconds 

Fall time 43e-9 seconds 

Constant voltage drop 0 V 

Power Diode QuietIR series 20 ETF 

Forward voltage drop 1.2 V 

On-resistance 0 ohm 

rms reverse voltage 21 V 

Snappiness factor 0.6 

Rate of fall forward current 100e6 A/s 

Reverse recovery time 60e-9 seconds 

Controller 

Frequency switching 10 kHz 

Modulation index 0.5 

Power factor motor 0.8 

2.4 Battery 

A battery is a device that converts chemical energy into electrical energy and vice versa. In 

this paper, a generic battery model will be used. The model is a modification of the 

Sheppard discharge battery model introduced by [26]. The battery is modeled using a 

controlled voltage source in series with internal resistance, as shown in Figure 5. 

Figure 5. Generic battery model. 

This model can represent the behavior of different battery types. The parameters of this 

model can be extracted from the discharge curve data. This model is based on several 

assumptions: the model has the same characteristic of charge and discharge cycles, the 

model has constant internal resistance and there is no Peukert effect; the battery capacity 

13


does not change with the amplitude of the current. 

The simulation used eight batteries as energy storage with the capacity was 36 Ah and the 

internal resistance of the batteries was 0.045 Ω. 

2.5 Regenerative Braking 

Regenerative braking is a mechanism to reduce the vehicle speed by converting some of its 

kinetic energy to other useful form of energy [27]-[29]. This converted energy can be used to 

charge the energy storage in the system, such as a battery or a capacitor. The regenerative 

braking is different from an auxiliary drive braking, where the electrical energy is dissipated 

as heat by passing current through large bank of variable resistors. 

The total energy dissipation is limited by either the capacity of the supply system to absorb 

this energy or by the SOC of the battery. If SOC of the battery is full, the auxiliary drive 

braking will absorb the excess energy. In order to capture the regenerative braking energy, 

the total traction torque must be negative. 

3 Load Profile of the Electric Drive Systems 

The load used in this paper was drive cycle of ICE karting at race day for one lap (48 

seconds). This drive cycle had previously been measured by the Flap track software at 

Göteborg karting ring track. The speed profile of the ICE karting for one lap can be seen in 

Figure 6. The drive shall be optimized for 10 minutes heat in full race and 3 minutes for in 

and out laps. 

Figure 6. Speed profile of ICE karting at Göteborg karting ring track 

According to this speed characteristic, the traction torque at the wheel is varying between 47 

Nm and -78 Nm. The negative torque indicates that the ICE karting is in deceleration or in 

regenerative braking region. It is clear that the regenerative braking or deceleration torque 

is higher than the acceleration torque. Therefore, the regenerative power used in charging 

the battery must be limited due to the limitation of the electric motor, power electronic 

converter and battery capability [27]. 

4 Modeling of the Electric drive system 

The model of electric traction drive system for electric karting is implemented using 

14


Matlab ® /Simulink ® software. A steady-state model is used to get raw data that is helpful 

during the design stage and for long-term analysis over an extended drive cycle. The 

advantage of this modeling is fast computation. The steady-state model is suitable to model 

the efficiency and performance of the system. 

This simulation ran in 48 seconds and used variable-step ode45 (Dormant-Prince) solver. 

The relative tolerance was 1e-3. There were also three m files that each represents the 

system parameters, displays the post processing, and includes calculation on the 

performance of each component of the system. 

5 Result and Discussion 

According to the data sheet of the electric motor, the rated of the motor is 2850 rpm, 6000 

W and 20.1 Nm. The speed and torque at the wheel were between 886 - 1801 rpm and 0 - 

47 Nm respectively, as explained. This speed and torque must be geared to values that have 

a high efficiency of the electric motor. By using, the gear ratio used in this drive system has 

been selected to 45/21. With this gear ratio, the induction motor operates at speeds 

between 1889 – 3859 rpm and torques between 0 – 21.9 Nm. The slip of the induction 

motor was assumed to be constant because the electric motor operates almost in the 

torque constant region. The average efficiency of the electric motor was 86.2%. The average 

efficiencies of the power electronic converter and the battery were 92.7% and 83.5%, 

respectively as seen in Figure 7. Thus, the total average efficiency of this system was 66.7%. 

Figure 7. Efficiency of the drive system 

The lower efficiency at the battery occurred as a result of the large current in the equivalent 

internal resistance of the battery. At regenerative braking condition, the efficiency of the 

electric drive system was lower than that of normal operation due to the larger current in 

regenerative braking. Therefore, at regenerative braking condition, the losses increased in 

the power electronic converter, battery and electric motor. 

The total energy used by the drive system is the subtraction of the regenerative energy from 

the used driving energy and can be seen in Figure 8. 

15


Figure 8. Energy used of the drive system. 

Figure 8 shows that the regenerative braking supplies a higher energy level in short time 

compared to normal operation. Consequently, this energy must be considered in designing 

an electric traction drive system. The comparison of energy used can be found at Table 4. 

Table 4. Comparison of energy usage 

Reg W/o reg [Wh] With reg [Wh] 

[Wh] a lap 13 min a lap 13 min 

Transmission 18.8 48.7 796.3 30.2 490.8 

Motor 15 53.5 869.4 38.5 626.2 

PEC 13.7 57.2 929.4 42 706.1 

Battery 11.4 67.9 1104.5 56.6 920 

Table 4 shows that the total regenerative energy at the battery is lower than the total 

regenerative braking energy at the electric motor due to losses at the components of the 

electric drive system. The energy needed to operate the electric karting for 13 minutes was 

920 Wh with regenerative braking, or 1104.5 Wh without regenerative braking. Therefore, 

the energy available in the battery (48Vdc) must be at minimum 19.2 Ah with regenerative 

braking and 23 Ah without regenerative braking. 

The performance of the battery in the drive system can be evaluated using the SOC of the 

battery during the drive cycle that can be seen in Figure 9. At the end of simulation, the SOC 

was 0.9639. If the drive cycle is assumed to be the same for other times, the storage energy 

can support the electric traction drive system for 23 minutes. It is clear that the regenerative 

braking energy can be used to charge the battery and reduce the energy usage in the 

system. 

A power and torque characteristics as function of the speed can be seen in Figure 10. The 

transmitted power in the electric traction drive cycle increases with the increasing speed 

due to increasing frequency and voltage until the rated speed. The maximum power occurs 

in the rated speed. At above the rated speed, the transmitted power decreases again as a 

16


result of the decreasing stator and rotor currents. The torque is almost constant at rated 

torque below the rated speed and will decrease at above the rated speed due to decreasing 

power transmitted in the electric karting. At regenerative braking condition, the torque is 

almost constant above the rated torque of the electric motor. 

6 Conclusion 

Figure 9. State of charge of battery at the drive system 

Figure 10. Speed – power and torque characteristics of the drive system 

The complete electric traction drive system was simulated and observed. The total average 

efficiency of the system was 66.7% and the total efficiency depended on the efficiency of 

the electric motor and the battery. In this simulation, there was no limitation for regenerative 

braking energy feed into the electric motor. The average power of the electric motor was 

found to be 5.4 kW. The total energy consumed for this electric traction drive system was 

56.61 Wh in one lap with the regenerative braking energy and 920 Wh in the whole race. 

17


The type of battery can be changed to other types of battery which have a lower internal 

resistance but the price will be higher. 

In this paper, the main focus has been placed on the induction motor modeling using the 

efficiency model on steady state. Thus, there exists much future research scope in 

improving the behavior of the electric motor, power electronic converter, and battery for 

dynamics simulation. Furthermore, the use of an advanced traction motor such as the 

permanent magnet DC motor, permanent magnet synchronous motor, series DC motor, 

brushless DC motor, and switched reluctance motor, which have even higher efficiencies, 

might lead to higher system efficiencies. 

7 References 

[1] J. Larminie, J. Lowry, Electric Vehicle Technology Explained, John Wiley and Sons, 

2003 

[2] C. C. Chen, “An overview of electric vehicle technology,” Proceeding of IEEE, vol. 81, 

no. 9, pp. 1202-1213, Sept 1993. 

[3] M. Ehsani, K. M. Rahman, H. A. Toliyat, “Propulasion system design of electric and 

hybrid vehicles,” IEEE Trans on Industrial electronics, VOL. 44, NO. 1, pp. 19-27,Feb 

1997 

[4] http://en.wikipedia.org/wiki/Go_karting last visited 25-2-09 

[5] http://www.kartelec.com/f/en_actu.htm last visited 25-2-09 

[6] C. Cardoso, J. Ferriera, V. Alves, R. E. Araujo, “ The design and implementation of an 

electric go-kart for education in motor control, “ IEEE International SPEEDAM 2006, 

pp. 1489 – 1494, May 2006. 

[7] F. J. Perez-Pinal, C. Nunez, R. Alvarez, M. Gallegos, “ Step by step design procedure of 

an independent-wheeled small EV applying EVLS,” IECON 2006-32nd Annual 

Conference on IEEE Industrial Electronics, pp. 1176-1181, Nov 2006. 

[8] M. Xianmin, “Propulsion system control and simulation of electric vehicle in MATLAB 

software environment, “Proceeding of the 4th World Congress on Intelligent Control 

and Automation 2002, pp. 815-818, June 2002. 

[9] J. M. Lee, B. H. Co, “ Modeling and simulation of electric Vehicle power system, “ 

Proceeding of the 32nd Intersociety IECEC-97, vol. 3, pp. 2005-2010, August 1997 

[10] S.S. Williamson, A. Emadi, K. Rajashekara, “Comprehensive Efficiency Modeling of 

Electric Traction Motor Drives for Hybrid Electric Vehicles Propulsion Applications,” 

IEEE Transaction on Vehicular Technology, vol. 56, no. 4, pp.1561-1572, July 2007. 

[11] Robert Bosch GmbH, BOSCH-Automotive Handbook, Robert Bosch GmbH, German, 

2002 

[12] G. C. D. Sousa, B. K. Bose, “ Loss modeling of converter induction machine system for 

variable speed drive,” Proceeding of the 1992 International Conference on Power 

electronics and Motion Control, vol. 1, pp. 114-120, Nov. 1992 

[13] Mohamed A. El-Sharkawi, ”Fundamentals of Electric Drives,” Brooks/Cole Thomson 

Learning, 2000. 

[14] J.J. Cathey, ”Electric Machines Analysis and Design Applying Matlab®,” McGraw Hill, 

2001 

[15] C. Shumei, L. Cheng, S. Liwei,” Study on Efficiency Calculation Model of Induction 

Motors for Electric Vehicles,” IEEE Vehicle Power and Propulsion Conference, pp. 1-5, 

Sept 2008 

[16] J. Faiz, M. B. B. Sharifian, “Optimal design of an induction motor for an electric 

vehicle, “Euro. Trans. Electr. Power 2006, vol. 16, pp. 15-33, July 2005. 

[17] G. Pugsley, C. Chillet, A. Fonseca, A-L. Bui-Van, “New modeling methodology for 

18


induction machine efficiency mapping for hybrid vehicles,” IEEE International Electric 

Machines and Drives Conference 2003, vol.2, pp. 776-781, June 2003. 

[18] S.M. Lukic, A. Emado, “ Modeling of Electric Machines for Automotive Applications 

Using Efficiency Maps,” Electrical Insulation Conference and Electrical Manufacturing 

& Coil Winding Technology Conference 2003, pp. 543-550, Sept. 2003. 

[19] N. Mohan, T. M. undeland, and W. P. Robbins, ”Power Electronics: Converter, 

Applications, and Design,” Hoboken, NJ:Wiley, Oct 2002 

[20] B.K. Bose, “Power Electronics and Variable Frequency Drives,” IEEE Press, New York, 

1997. 

[21] B.K. Bose, “Modern Power Electronics and AC Drives,”Prentice Hall, New York, 2002. 

[22] I. Husain, M. S. Islam, “Design, Modeling and Simulation of an Electric Vehicle 

System, “SAE-Advanced in Electric Vehicle Technology, 1999-01-1149, March 1999. 

[23] Ali Emadi, “ Handbook of Automotive Power Electronics and Motor Drives,” CRC Press 

– Taylor and Francis Groups, Florida 2005 

[24] F. Casanellas ,”Losses in PWM inverter using IGBTs,” IEE Proc. Electr. Power Appl., 

vol. 141, no. 5, pp. 235-239, September 1994. 

[25] P.A. Dahono, Y. Sato, T. Kataoka,” Analysis of conduction losses in inverter,” IEE Proc. 

Electr. Power Appl., vol. 142, no. 4, pp. 225-232, July 1995 

[26] O. Tremblay, L-A. Dessaint. A-I. Dekkiche, “A generic battery model for the dynamic 

simulation of hybrid electric vehicles, “IEEE Conference on Vehicles Power and 

Propulsion VPPC 2007, pp. 284-289, Sept. 2007. 

[27] J. Lee, D. J. Nelson, “Rotating inertia impact on propulsion and regenerative braking 

for electric motor driven vehicles,” IEEE conference on Vehicle Power and Propulsion 

2005, pp. 308-314, Sept 2005. 

[28] B. Cao, Z. Bai, W. Zhang, “Research on control for regenerative braking of electric 

vehicle,” IEEE International Conference on Vehicular Electronics and Safety 2005, pp. 

92-97, Oct 2005. 

[29] Z. Junzhi, L. Xin, C. Shanglou, Z. Pengjun, “Coordinated control for regenerative 

braking system,” IEEE Vehicle Power and Propulsion Conference (VPPC), pp 1-6, Oct. 

2008 

19


Feasibility Study of Solar Power Massive Usage in Indonesia: Yield 

versus Cost-Effective 

Abstract 

M. A. Setiawan 

Politeknik Manufaktur Timah 

Jln. Timah Raya Air Kantung Sungailiat Bangka Indonesia 

Email : made_andik_s@plasa.com, made@polman-timah.ac.id 

The aim of this study is to analyze the cost production of solar power utilization comparing with its 

annual yield especially in Indonesia. Solar cell module employed is poly-crystalline silicone with Peak 

Power 20 Wp, Power Current (Imp) 1.17 A and Power Voltage (Vmp) 17.1 V. To obtain the maximum 

power of the sun, the module is static fixed in 1 0-2 0 N adjusting to the equator line. The 

measurement is conducted in Timah Manufacture Polytechnic which is situated in 1 020’ S and 106 0 

E. The output is observed by multimeter data logger for every hour average. The cable employed 

between solar module and the multimeter and the battery is NYA Eterna 2.5 mm 2 450/750 V with 

SNI number 04-2698 SPLN 42, the long is 40 meter and have resistance about 0.6-0.7 Ω. The 

measurement output indicates that the maximum solar power is at 11.00 to 14.00 WIB. In these 

times, the current output is more than Imp of the Module. By calculating the average of energy 

received in a day, the energy received is 65%-75% of Imp. Therefore by mathematically calculation, 

the annual yield of current is about 4,982.68 Ampere and in 25 years will be around 124,566.90 

Ampere. According to PLN statistic report in 2008, the average cost production in 2007 is 

Rp.706.62/KWH and the cost production of the solar energy is about Rp.440.819/KWH. This 

calculation is included the investment, overhead and 10% of inflation /year. By comparing to the 

regional minimum revenue (UMR) per month in 2008, the cost of investment for the solar power 

usage is about 6-7 times. Although break-even-point will be occurs in 10 years, the affordability of 

Indonesian for massive usage of solar power is still too hard and need funded by government and 

others funding groups. 

Keywords: Solar Cell, Cost-Effective, Poly-Crystalline-Silicone, Annual Yield 


Solar power is the one of potential renewable energy sources in the future as stated by 

Mark Clayton [1] "Solar power is the energy of the future - and always will be". The massive 

usage of the renewable energy is not always in term of technology but also in term of costeffective 

[2,3] : how the source could provide the affordable energy. 

Indonesia is situated in tropical area which receives a lot of solar energy every year. 

Unfortunately, the applying of solar energy as the one energy sources is still have many 

problems such low efficiency and more expensive than fossil fuel [4]. Another problem is 

that there is no actual data in experimental based which shows the actual solar energy 

received annually. Thus the massive usage of solar power in term of cost-effective is hard to 

be determined. 

This study is to determine and evaluate the annual yield of solar energy by experimentally 

measurement. Obtaining annual yield is useful to analyze the potential energy sources 

21


based on employing mass technology equipments. After that, the costeffective of the solar 

power especially in Indonesia could be predicted. 

2 Literature Review 

The main advantages of solar power is “quite operation, good reliability”, and high scalable 

[4], “abundant, clean, renewable energy” [2], and also “almost pollution- free” [3]. Solar 

energy also will reduce greenhouse gas emission 1.7 million tons and 1.9 million tons car 

gas emission every year and estimated in the 2050, carbon dioxide emissions will reduce 

up-to 62% than emission in the 2005 [3]. 

The most used solar energy is crystalline silicon, thin-film solar cell and solar concentrator 

[5]. Solar concentrator is technology which uses mirror to mix a light in “high-performance 

and sensitivity” area [6]. Another technique is presented by Marc Dalbo from MIT laboratory 

[7]. This technology is called dye molecule. Dye Molecule is to transmit a light from the sun 

in different wave length as well as be used in fiber optic communication. The energy of the 

light is more powerful than in original wave length. 

The investment of applying solar energy today is booming [6]. In 2008 the solar power 

usage is increased to 55% than in 2007. United States government has been allocated 

funding more than $400 billion for solar power investment up to 2050 [4]. 

The scientists also conduct intensive research to increase the efficiency of solar cell 

technology. Scientists in National Renewable Energy Laboratory (NREL) claim that they have 

produced a solar cell which has efficiency up to 40.8% [8]. Marc Dalbo with his Dye 

Molecule claims that his technology efficiency can be 50% [7]. General efficiency 

development of solar cell is presented by Figure 1. 

Figure 1. Solar cell technologies and its efficiency [8] 

22


3 Materials And Methodology 

This study is conducted in Timah Manufacture Polytechnic, Sungailiat – Bangka - Indonesia. 

Bangka Island is situated in 1020’-307’ S and 1050-1070 E. The satellite map of Bangka 

Island from Google Earth in 2008 is illustrated in Figure 2. 

Figure 2. Satellite map of Bangka Island from Google Earth and solar cell fixed in campus. 

Solar cell employed is 2 modules of poly-crystalline silicon which has specification: 

a. Module Type : 1-051Z-00067 

b. Peak Power (pmp) : 20 W pmp 

c. Max Power Current (Imp) : 1.17 A 

d. Max Power Voltage (Vmp) : 17.1 V 

e. Short Circuit Current (Isc) : 1.29 A 

f. Open Circuit Volatge (Vop) : 21.5 V 

g. Nominal Temperature Cell (Tnoct) : 25 0 Celcius 

h. Test Data Condition : E=1000 W/m 2 , Tc=25 0 C 

This technology is employed due to the affordability of Indonesian, components reliability 

and space needed. The others technology need more expensive in investment, wider space 

in installation and harder in components reliability. 

To obtain the maximum sun light power, the module is fixed to the equator line. The module 

fixed in 1 0 – 2 0 N because the campus location is in 1 0 – 2 0 S. The module employed and its 

setup is illustrated in Figure 3. 

Configuration of components used in this study is solar cell module, multi-meter (current 

and voltage), battery, inverter and DC/AC loads. The equipments configuration of this study 

is presented in Figure 4. 

Figure 3. Setup and solar cell module 

23


Figure 4. Equipments configuration 

The output of the solar cell module is in Ampere and in Volt, but the current received on the 

multi-meter will be depended and deducted by resistance of the cable. According to Ohm’s 

law, resistance of the cable depend on length, diameter and materials of the cable. To 

eliminate the resistance as much as possible, the diameter of the cable is chosen which has 

a bigger diameter. The affordable cable in Bangka’s market is maximum 2.5 mm 2 of 

diameter. In this study the cable used has type NYA Eterna 2,5 mm 2 450/750 V, SNI 

number 04-2698 SPLN 42 and 40 meter of length. This cable has 0,6-0,7 Ω of resistance. 

The battery employed is GS Astra with 70 AH. 

4 Results And Discussions 

The output of the solar module is sampled for every hour from 08.00 WIB to 16.00 WIB. The 

measurements will be conducted in a year to obtain actual annual yield. The results of these 

measurements are presented in Figure 5 and 6. 

Ampere 

2.50 

2.00 

1.50 

1.00 

0.50 

0.00 

Current Yield 

8 9 10 11 12 13 14 15 16 

Time (WIB) 

Figure 5. Current output of the solar cell module 

24 

July 

August 

Sept 

Imp


Figure 6. Voltage output of the solar cell module 

The results indicate that: 

a. The maximum current is received between 11.00 WIB and 14.00 WIB. 

b. By mathematically calculation, the average energy received for a day is around 65% 

to 75% of Imp times by hours. 

c. The voltage output of the solar cell module is likely to be not much different each 

others. 

According to daily yield of the energy, it can be mathematically calculated that in toward 5 

year the production cost will be only for overhead and total ampere will be around 

6.411.000 A. The comparison of production cost and energy yield is presented in Table 1. 

Table 1. Cost production and energy yield estimation up to 25 years 

Year 1 5 10 15 20 25 

Ampere 4,982.68 24,913.38 49,826.76 74,740.14 99,653.52 124,566.90 

Cost (Rp) 4,940,000 6,411,000 8,172,100 9,964,110 12,080,521 12,130,521 

According to PLN statistic report in 2008, the average cost production of power in Indonesia 

in 2007 is Rp. 706.62/ KWH. By mathematically calculation, the cost production of the 

solar energy in this study is Rp. 440.819 /KWH. This cost is cheaper than the average 

production cost of the PLN but equal with the cost production of natural others resources. 

The cost of solar energy is determined by calculating the overhead, components 

replacements, and 10% of inflations. 

Based on those calculations, the comparison of the costs up to 25 years is illustrated in 

Figure 7. The Figure 7 indicates that the break-event-point of the solar energy will be 

occurred in 10-15 years. After that the cost production of the solar energy will be cheaper 

than fossil fuel energy used by PLN. 

Although the cost production of solar energy will be cheaper than fossil fuel in toward 10-15 

years, but the cost of investment in the beginning year is out of the affordable of General 

Indonesian. According to BPS Bangka province statistic report in 2008, the regional 

25


minimum revenue (UMR) in Bangka province is about 1,1 million rupiah. Thus solar energy 

cost investment is about 4-6 times than UMR and become harder for their affordability. 

Indonesian Rupiah 

18,000,000 

16,000,000 

14,000,000 

12,000,000 

10,000,000 

8,000,000 

6,000,000 

4,000,000 

2,000,000 

- 

The Cost Estimation 

1 5 10 

Year 

15 20 

Figure 7. The cost comparison of thePLN and the solar energy. 

26 

Solar Energy 

PLN (Fossil Fuel) 

5 Conclusions 

This study is to determine and evaluate the annual yield of solar energy by experimentally 

measurement. Obtaining annual yield is useful to analyze the potential energy sources 

based on employing mass technology equipments. After that, the costeffective of the solar 

power especially in Indonesia could be predicted. 

The measurements are conducted by sampling average every hour and every day from 

08.00-16.00 WIB. The solar module employed is poly-crystalline silicone with Peak Power 

20 Wp and Power Current (Imp) 1.17 A. The average daily yield is about 65%-75% Imp times 

by sum of hours. 

The break-event-point of solar energy will be occurred in toward 10-15 years. The cost 

production of solar energy is Rp. 440.819/KWH cheaper than PLN cost production. But, the 

cost of investment of the solar energy in the beginning year is more than 4-6 times than 

regional minimum revenue (UMR), therefore the massive usage of solar energy for 

Indonesian will be harder to be realized. The government and funding group is needed to 

involve in this project. 

6 References 

[1] Mark Clayton, “Solar edges closer to 'grid parity'”, The Christian Science Monitor, p25, 

2008. 

[2] Nancy Spring, “Solar Performing Brilliantly”, Electric Light and Power, 5(86), p50, 2008. 

[3] Ken Zweibel, James Mason, Vasilis Fthenakis, “Solar Grand Plan”, Rachel's Democracy 

& Health News, (976), p1, 2008 

[4] Jeffrey Winters, “the sunshine solution”, Mechanical Engineering, 12(130), pp24-29, 

2008.


[5] Dean Takahashi, “Solar Boom”, Technology Review, 5(111), p30, 2008 

[6] Gerald Parkinson, “Cost-Effective Devices Open A New Window on Solar Energy”, 

Chemical Engineering Progress, 8(104), p14, 2008. 

[7] Kevin Bullis, “Intensifying the Sun”, Technology Review, 5(111), p104, 2008. 

[8] Suzanne Shelley, “Solar Cell Sets World Efficiency Record at Over 40%”, Chemical 

Engineering Progress, 9(104), p18, 2008. 

27


Modelling and Designing The Model Predictive Control System of 

Turbine Angular Speed at Hydropowerplant UBP Saguling PT 

Indonesia Power 

R.K.A Kusumah, E. Joelianto, E. Ekawati 

Research Division of Instrumentation and Control – Faculty of Industrial Technology; 

Bandung Institute of Technology; Jl. Ganesha 10, Bandung, 40132, Indonesia 

Astract 

Saguling Generation Business Unit (GBU) is one of hydro powerplants under PT. Indonesia Power 

which has vital role to produce and distribute electricity in Indonesia. The demand for electricity in 

Indonesia, which is fluctuative, force the plant to operate in immediate and responsive pattern. 

Saguling need 2 minute to connect to the transmission system from its non operating state. Plant 

response is controlled by manipulating guide vane opening so the water entering the turbine chamber 

can be maintained. 5.6 % maximum overshoot still occurs in start up process due to manual 

mechanism. This paper provide a design of control system using Model Predictive Control (MPC) to 

optimize the plant performance which is indicated by faster response time and reduced overshoot. 

Neural Network with Back Propagation algorithm is used to model the turbine with guide vane 

opening as input variable and turbine angular speed as output variable. The model is then used in 

MPC algorithm to compute the optimum control signals. 

Keywords: Neural Network, Model Predictive Control, Guide Vane, Cost Function. 


Saguling GBU operates only when demand for electricity occurs. Fast response time (2 

minute) from non operating state to connected state has become an advantage for Saguling 

plant compare to similar plant with different generating source. The plant also contribute in 

maintaining transmission system frequency stability, besides its main role to produce 

electricity [1]. 

Generally, in order to transmit voltage and at the same time maintain frequency stability, 

hydro powerplant must be connected to the transmission system. For that purpose, the 

plant frequency must be made equal to the transmission system frequency. This sequence 

is done manually. Once it is connected, the whole process, including electricity producing, is 

done automatically. 

Controller is modeled and designed with hope that it would increase plant’s performance 

rather than controlled manually. The criterion chosen to evaluate plant’s performance are 

settling time and the existence of maximum overshoot. The basic consideration for 

choosing these criterions is because system response is clearly seen based on these 

criterions. In addition, at present time, system response still has overshoot and long 

duration of settling time. System is modeled using neural network with backpropagation 

algorithm while Model Predictive Control is used to designed the controller. 

2 Hydro Powerplant Fundamentals 

29


2.1 Basic Principle 

Frequency is a significant aspect in hydro powerplant. Initially, frequency has to be made 

synchronized with the transmission system frequency, which is 50 Hz. Once it is connected, 

plant’s frequency will be influenced by transmission system frequency due to small capacity 

in power production that the plant’s generate, relative to overall power consumption [2]. If 

there is an increasing in demand for electrical power, there will be an increasing in amount 

of power transferred from the powerplant as well. Plant’s frequency will gradually decreased 

in result. This frequency fluctuation will affect the turbine angular speed. 

2.2. Control Principle 

Hydro powerplant has two sequence of control, there are control in start up condition and in 

synchronize condition. In start up condition, the plant is not connected to the transmission 

system. It must first maintain its frequency until it met the requirement to get to the next 

sequence. This should be done in order to avoid damage to the generator due to phase 

difference between generator frequency and transmission system frequency. In start up 

condition, the main purpose is to maintain turbine angular speed in its angular speed 

nominal, which is 333 rotation per minutes, equals to 50 Hz. Once this nominal is aqcuired, 

system is ready to connect to the transmission system. Fig.1 illustrate the control loop of 

hydro powerplant. 

3 Identification System 

Figure 1. Block diagram of Saguling GBU control system 

3.1 Identification Fundamentals 

Identification is a process to find model of a process or system based on experimental data 

provided. With such model, the characteristics of the system can be known and analyzed 

[3]. Fig.2 shows the identification steps. 

Identification process first begin by designing an appropriate experiment which later sets of 

data will be aqcuisited from the experiment. The data obtained from variables entering the 

process and variables leaving the process. The next step is to choose the model structure, 

30


including the order of the system and choosing the estimation parameter method. 

Afterwards, the model should be validated. 

Figure 2. Identification steps 

Turbine system was choosed because in startup condition, the process was focused in 

controlling the turbine angular speed. Fig.3 illustrate the relation between the input 

variable, the process and the output variable. 

Figure 3. Turbine system 

3.2 Neural Network 

Neural network is a system which modeled human nerves system as a continuous nonlinear 

dynamic system. This network has nodes analogues with neuron in brain. 

Mathematical processes used in this network are also an approach to the way how brain 

works and also has the ability to learn from experience. Fig.4 illustrate the structure of 

neural network, 

Figure 4. General neural network structure 

31


A simple neural network structure can be expressed in, 

y 

⎜ 

⎛ n 

f W U + b⎟ 

⎞ 

⎝ i 1 

i i 

⎠ 

= ∑ = 

W1, W2, ….Wn and Wb are neural network weighthing parameters. These values will be 

evaluated in each iteration process. U1, U2, ... Un are neural network inputs. Each one of 

them will be multiplied with weight W accordingly, which in turn will be summed and 

calculated with an activation function F. In result, the output Y will be known. 

3.3 Backpropagation Algorithm 

The main idea of this algorithm is to evaluate and modify the weights and bias in a way so 

the error value minimized. First step in this algorithm is choosing the cost function. Error is 

represented as the difference between desired output and output obtained from neural 

network learning. One of the cost functions used is the sum of quadratic error which defined 

in equation, 

( ) 2 

1 

E = 

2 ∑ O − 

d 

d y d 

(2) 

where Od and yd are the desired output and output obtained from neural network 

respectively. To minimize the cost function, the weights are evaluated with, 

∂E 

W( 

k + 1 ) = W( 

k) 

−η 

(3) 

∂W 

where W is weight, is learning rate and E is cost function in error quadratic form. On outer 

layer, the gradient of cost function to weight is, 

∂E 

∂W 

O 

dj 

= − 

32 

(1) 

d 

∑ ( Od 

− y d ) (4) 

O 

where is the weight connecting the d- neuron from outer layer to j-neuron from hidden 

layer. 

3.4 Identfication Using Neural Network 

System was identified with previous acquisited data in non real time condition. 2250 

number of data were used as turbine input and output, while guide vane opening was the 

input variable and turbine angular speed as the output variable. Time sampling used was 

0.1 seconds. Identification process was done using neural network. Assumed a first order 

system with time delay. Based on this apriori knowledge, a neural network structure can be 

formed to be identified. 

d 

∂y 

∂W 

Based on (1), Saguling turbine system model can be illustrated with Fig. 5, 

dj


And in mathematical equation, 

Figure 5. ADALINE structure 

Due to delay time in system response, (5) become, 

y ( k) 

= −y( 

t −1) 

a + u( 

t −1) 

b 

(5) 

33 

1 

y ( k + nk) 

= −y( 

k −1+ 

nk) 

a + u( 

k −1) 

b 

(6) 

with nk is the delay time, which values nk = 4 seconds. Fig. 4 shows the model obtained 

from identification process while Fig. 6 shows validated model. 

Figure 6. Model from NN learning and actual response 

1 

1 

1


Figure 7. Model validation graphic 

Root mean square error (RMSE) was used to analyze the closeness degree between 

identification result and the real value. RMSE defined by, 

RMSE = 

N 

∑ i= 

1 

34 

( y( 

ˆi 

) − yˆ 

( ˆi 

) ) 

Table 1 shows the identification parameters result, 

Table 1 Identification process parameters 

PARAMETER VALUE 

Data used for learning 1100 

Data used for validation 2250 

Learning Rate 10.5 x 10 -7 

Epoch 1500 

RMSE from learning 1.72 

RMSE from validation 0.89 

RMSE parameter in Table I shows the error value for each tracing point obtained between 

the model and the actual data. Saguling GBU can tolerate error value to 2 % which equals to 

range of response the systems can handle, that is 326.4 RPM to 339.66 RPM. From 

validation result, the system response vary from 332.11 RPM to 333.89 RPM, which means 

the error range still tolerable. Therefore, the model can be used to represent the process 

dynamics of the system. 

The transfer function obtained from identification is, 

N 

2 

(7)


2 

0. 

02847z 

− 0. 

1137z 

+ 0. 

881 

H ( z) 

= 

(8) 

2 

z −1. 

995z 

+ 0. 

9949 

Information about delay time was already included in above equation. To design the 

controller, the model above should be multiplied with the actuator transfer function which 

was already known. Below equation is the actuator transfer function[6], 

0. 

003221 

G ( z) 

= (9) 

z − 0. 

9968 

Thus, the whole process and the actuator transfer function become, 

9. 

16× 

10 z − 3. 

66× 

10 z + 2. 

83× 

10 

P ( z) 

= G( 

z) 

⋅H( 

z) 

= 

(10) 

3 

2 

z − 2. 

992z 

+ 2. 

983z 

− 0. 

9917 

35 

−5 

4 Cotroller Design 

4.1 Model Predictive Control (MPC) 

MPC is a control strategy which designed based on model of certain processes. The model 

is used to calculate a set of future prediction output based on set of control signals given to 

the model. By using an optimization algorithm to minimize MPC cost function, a set of 

control signal can be obtained. Thus, controler performance really depends on the 

availability of a good model[4]. 

Model used in MPC in descrete state-space form can be represented as, 

x d 

y d 

z z 

2 

−4 

( k +1 ) = Ad 

x( 

k) 

+ B u( 

k) 

(11) 

( k) 

= C x( 

k) 

(12) 

( k) 

= C x( 

k) 

(13) 

is state of the system, Ad, Bd are output matrices, Cd and Cz is observable and 

controllable output matrice of the system respectively. Furthermore, prediciton output can 

be obtained by iterating a model defined by, 

ˆz 

( k + i| 

k) 

= C ˆx 

( k + i| 

k) 

= C 

z 

z 

A 

i 

d 

x + 

∑ 

i 

j= 

1 

C 

z 

A 

j−1 

d 

−4 

B uˆ 

( k + i − j| 

k) 

Output prediction for the next k+j step, where state of the system on step k assumed to be 

known, can be recursively done and represented in matrice form, 

d 

(14)


⎡ zˆ 

( k + 1| 

k) 

⎤ 

C B 

⎡ C A 

x d 

x d ⎤ 

⎡ 

⎤ 

⎢ 

ˆ 

⎥ 

z( 

k 2| 

k) 

⎢ 2 

C A 

⎥ 

⎢ 

C x Ad 

Bd 

+ C B 

⎥ 

x d 

⎢ 

+ 

⎥ x d ⎢ 

⎥ 

= ⎢ ⎥ x( 

k) 

+ 

u( 

k −1) 

⎢ M ⎥ ⎢ M ⎥ 

⎢ M ⎥ 

⎢ − 2 i 

⎢ 

⎥ ⎢ 

H 

H ⎥ 

⎥ 

v 

zˆ 

( k H | k) 

C A v 

⎢ 

C A B 

⎣ + v ⎥⎦ 

⎣ x d ⎦ ⎢∑ 

i 

x d d ⎥ 

= 0 ⎣ 

⎦ 

⎡ C xB 

d 

⎢ 

C x Ad 

Bd 

+ C x B 

⎢ 

d 

+ ⎢ M 

⎢ − 2 i 

Hv 

⎢∑ 

C A B 

i 0 

x d d 

⎣ = 

L 

L 

O 

L 

0 ⎤ Δuˆ 

( k| 

k) 

0 

⎥ 

⎥ Δuˆ 

( k + 1| 

k) 

M ⎥ 

⎥ 

M 

C xB 

d ⎥ Δuˆ 

( k + Hv 

−1| 

k) 

⎦ 

With Δ u ˆ( k + i| 

k) 

is incremental input, which is Δ uˆ ( k + i| 

k) 

= uˆ 

( k + i| 

k) 

− uˆ 

( k + i −1| 

k) 

. 

(15) can be simplified into [5], 

Zˆ 

( k) 

= ψ x( 

k) 

+ γ u( 

k −1) 

+ ΘΔUˆ 

(16) 

144244 

4 3 

The objective cost function is, 

36 

past 

{ future 

Hp 

2 Hu 

−1 

JMPC ∑ zˆ 

( k + i) 

| k) 

− w( 

k + i) 

+ 

i= 

H 

Q( 

i) 

∑ 

u 

i= 

0 

2 

R( 

i) 

(15) 

= Δuˆ 

( k + i| 

k) 

(17) 

with Q and R are weighting matrices. The optimum of Δ û can be obtained by finding the 

gradient equals to zero of JMPC. 

4.2 System Constraints 

In practices, all processes have their constraints, such as input constraints, output 

constraints, incremental input constraints and state-space constraints. These constraints 

can exist in form physical constraints, like actuator limit. 

The constraints can be expressed in form, 

u 

y 

x 

min 

Δu 

min 

min 

min 

≤ uˆ 

( k + i| 

k) 

≤ u 

≤ yˆ 

( k + i| 

k) 

≤ y 

≤ xˆ 

( k + i| 

k) 

≤ x 

max 

≤ Δuˆ 

( k + i| 

k) 

≤ Δu 

with umin, umax, ymin, ymax, xmin and xmax are minimum and maximum input, output and state 

constraints respectively. 

4. 3 Simulation 

Due to the necessity of MPC for a discrete model, (10) was discretized so it changed into, 

max 

max 

max 

(18)


−5 

9. 

16× 

10 z − 0. 

0003663z 

+ 0. 

0002837 

P ( z) 

= 

(19) 

3 

2 

z − 2. 

992z 

+ 2. 

983z 

− 0. 

9917 

37 

2 

Then (19) was transformed into state-space form so the value of each parameters in (11), 

(12) and (13) can be found, there are: 

⎡2 . 992 −1. 

492 0. 

4959⎤ 

⎢ 

⎥ 

• A = 

⎢ 

2 0 0 

⎥ 

⎢ 

⎣ 0 1 0 ⎥ 

⎦ 

• 

B = 

0. 

⎡ 

⎢ 

⎢ 

⎢ 

⎣ 

01563⎤ 

⎥ 

0 

⎥ 

0 ⎥ 

⎦ 

• C = [ 0 . 005868 − 0. 

01172 0. 

00908] 

• D = [] 0 

The next step was to apply the parameters into MPC algorithm. MPC was then tuned to 

obtain the desired transient response. The tuning was limited only on weight R because 

manipulating Q gave no significant effect. The tuning parameters used in this simulation 

were, 

• Maximum Horizon = 10 

• Control Horizon = 3 

• Minimum Input Constraint = -10 V 

• Maximum Input Constraint = 10 V 

• Weight Q = 40 X 40 Identity matrice 

Weight R was the parameter tuned to find the desired response. It is a 3 X 3 diagonal 

matrice which valued R=0.01, R=0.1 and R=1.0 respectively. Fig.8 and Fig. 9 shows the 

response and control signal comparation between manual control and system with MPC 

included as controller. 

(a)


38 

(b) 

(c) 

Figure 8. Comparation between manual vs system with MPC response with (a) R=0.01, (b) R=0.1 and 

(c) R=1.0 

(a)


39 

(b) 

(c) 

Figure 9. MPC control signal with (a) R=0.01, (b) R=0.1 (c) R=1.0 

Table 2 shows the simulation result, 

Table 2. System performance comparation with different weighting 

Performance Criterion 

R Settling Time 

(Sec) 

Maximum 

Overshoot 

0.01 55.6 None 

0.1 58.1 None 

1 70.1 None


As can be seen in Fig. 8, system response with MPC was faster compare to manual system. 

And it can be seen from Table II that settling time for MPC (55.6 sec, 58.1 sec and 70.1 

sec) was faster than manual control (120 sec). As R smaller, the response became faster 

yet the signal control became more fluctuative, which can be seen in Fig. 9. For mechanical 

system like turbine, an extreme fluctuation of control signals is undesirable due to actuator 

limitation. Therefore an appropriate value for R should be chosen carefully. In this 

experiment, the appropriate R would be 0.01. The control signal was qualitatively smooth 

enough and there were no overshoot. 

5 Summary 

From the simulation, a SISO turbine-generator system in Saguling GBU can be modeled, 

although there were many simplifications during identification process. The RMSE in 

identification process found to be 0.89, which is good enough to decide that the model 

accurately represent the real process. The model obtained was good enough to be used in 

simulation. Settling time criterion for system with MPC controller found to be 58.1 seconds, 

faster than using manual controller which is 120 seconds. In addition, system with MPC 

controller can eliminate the overshoot. 

6 References 

[1] Anonymous, Deskripsi Indonesia Power UBP Saguling, Februari 2009, 

www.indonesiapower.co.id 

[2] Zuhal. Dasar Tenaga Listrik, Penerbit ITB, Bandung, 1991. 

[3] Soderstrom, Torsten and Stoica, Peter. System Identification. Marylands Avenue : 

Prentice Hall International (UK) Ltd, 1989. 

[4] Maciejowski, J. M., Predictive Control with Constraints, England: Prentice Hall, 2000. 

[5] Ling, K. V., "Introduction to Model Predictive Control," course notes, Bandung Institute of 

Technology, May 2008. 

[6] Manual Handbook Sistem Governor UBP Mrica, HPC 610 Water Turbine Governor, Asea 

Generation, 1985. 

40


Performance Analysis of Finger Flexor and Finger Extensor 

Muscles on Wall Climbing Athletes through Electromyography 

Measurement, Handgrip Strength, Handgrip Endurance and 

Lactate Acid 

H. Susanti * , Suprijanto * , F. Idealistina * , and T. Apriantono # 

∗ Medical Instrumentation Laboratory and Instrumentation and Control Research Division 

Bandung Institute of Technology - Ganesha 10, Bandung 40132, Indonesia 

E-mail: supri@tf.itb.ac.id, hesty133@students.itb.ac.id 

# Sport Science Research Division, School of Pharmacy 

Bandung Institute of Technology 

Ganesha 10, Bandung 40132, Indonesia 

Abstract 

One of activities involved gripping activity is wall climbing sport. Physiologically, gripping activity 

involves finger flexor and finger extensor muscles at the lower arm. Naturally, muscles performance 

will decrease if muscles are given static or dynamic weight during certain time. This performance 

decrease is related with fatigue condition, which is also related with lactate acid accumulation, 

insufficient of metabolic reserve, and the decrease of neural activity in stimulating contraction[2]. 

Muscle fatigue characteristic experienced by participants were observed from the changing trend of 

Power Spectral Density (PSD) and median frequency of EMG signals, lactate acid level, and changing 

of handgrip strength and handgrip endurance values from four groups of measurement data. 

The experiment was involving two professional wall climbing athletes as participants. The 

measurements were performed four times, which were before and soon after climbing, after first 15 

minutes and after the second 15 minutes active recovery process, except for lactate acid level, 

measurements were performed twice, which were before and soon after climb activity. The athlete 

would have been ordered to climb the boulder for 15 minutes with a certain difficulty level. 

Muscle performance measurement involved in this gripping activity was very important to know how 

far the influence of difference of subject and period of active recovery to recover muscle condition. By 

this knowledge, a couch or an athlete can arrange an effective training strategy to reach a maximum 

achievement. 

Keywords: Electromyography, handgrip endurance, handgrip strength, Power Spectral Density, 

median frequency, finger flexor and finger extensor, lactate acid, fatigue 


Naturally, muscles performance will decrease if muscles are given static or dynamic weight 

during certain time. This performance decrease is related with fatigue condition, which is 

also related with lactate acid accumulation, insufficient of metabolic reserve, and the 

decrease of neural activity in stimulating contraction[2]. 

41


Muscle fatigue can be recovered by resting during certain time, which enable oxygen reavailable 

and lactate acid are transported by blood to the heart to retransform become 

pyruvic acid, and glucose are released for muscle glycogen supply[2]. 

When performed gripping activity, such as wall climbing sport, fatigue condition were 

observed from the descent of handgrip endurance, while handgrip endurance was a longest 

time to endure 70% from handgrip strength value. Handgrip strength was measured from 

the maximum value of three times right hand grip forces. 

Another parameter could describe fatigue condition was the changing of electrical activity 

from recording of EMG signals. EMG measurements were performed by using two sets 

bipolar surface electrodes, in finger flexor and finger extensor muscles, when the subjects 

performed gripping activity using handgrip dynamometer. Then, they will be completed by 

doing spectral analysis (in this case were Power Spectral Density and median frequency) to 

quantify muscle fatigue condition. 

The last parameter was the blood lactate acid accumulation. The measurements were 

performed in the use of lactate analyzer by taking blood sample from auricle. The lactate 

accumulation in blood when the subject experienced fatigue was different to another, based 

on many factors, such as exercise factor. 

The objective of this paper was to correlate handgrip dynamometer measurement and 

blood lactate accumulation with EMG signals parameters to describe fatigue condition. 

Besides, it would be observed the influencing signification of subject difference factor and 

the collecting data session with the two ways ANOVA statistical analysis. 

2 Basic Theory 

2.1 Muscular and Nervous System 

Muscular system is including skeletal muscles that arranged in functional cluster, adapting 

to do particular moves. Skeletal muscles are moved consciously under central nervous 

system control. This control system is regulated by a group of electric activities in nervous 

system[2]. The information from whole nervous system is sent by electric signals that being 

produced by electrochemical reaction. Electrochemical reaction is a reaction that can 

produce electric current. 

Nervous cell (neuron) is covered by semi-permeable membrane that works selectively on 

passing ion. Important ions in nervous system are sodium (Na + ), calcium (Ca 2+ ), chlor (Cl - ) 

and protein molecules with negative component. Those ions can move through semipermeable 

membrane thus effect electrostatic potential of nervous cell. 

When the nervous cell is having a rest, the potential intern cell will be more negative 

relatively to the outside (-70 mV). To produce a potential action, stimulus that should be 

given has to have intensity 15-20 mV, so it can exceed threshold level about -55 mV. 

2.2 Electromyography 

EMG signal comes from nervous electric activity when muscles contract or relax. Amplitude 

range of EMG signal is between 0-10 mV (peak to peak) or 0-1,5 mV (rms). While the rang 

42


frequency recorded by surface electrode is around 0-500 Hz, with dominant frequency 

between 50-150 Hz. EMG signal is affected by noise, they are electromagnetic radiance (50- 

60 Hz), motion artifact and instability because the randomness of unit motor activation 

phase.[3] The electric activity shape that measured in EMG (raw signals) is pictured in figure 

1. 

Figure 1. Recorded raw signal EMG 

2.3 Electrode 

Electrode used in this experiment was surface electrode Ag/AgCl. The configuration on 

putting bipolar electrodes is, positive and negative electrodes are put on the thick part of 

the muscle with gap space 1 cm, and reference electrodes put on the neutral which a bump 

bone relatively far from those other electrodes. Voltage that measured is the gap between 

positive electrode voltage to reference and negative electrode voltage to reference. 

2.4 EMG Signal Parameter 

EMG signal parameters that would be analyzed are mean Power Spectral Density (PSD) and 

median frequency (MF). Mean PSD is determined through classical spectral estimation 

technique with Fourier transform, which is periodogram Welch method. Estimation result is 

got from equation (1). Then, it is calculated its estimated PSD value in every experimented 

frequency and for every data. The estimated PSD values for every data will be calculated the 

average for every experimented frequency, it is what we called mean PSD. This mean PSD 

will relatively increase with the ascending of muscle fatigue[1]. 

P 

ˆ 

~ ( 

Pw 

( fi 

) P 

P ∑ −1 

1 

= 

43 

p= 

0 

MF is frequency that divides PSD into two parts which have equal power. MF can be 

abbreviated to equation (2). MF values will relatively decreasing with the ascending of 

muscle fatigue[4]. 

p) 

xx 

( f) 

(1)


fmed 

∫ 

0 

S 

m 

( f) 

df = ∫ S 

44 

∞ 

fmed 

m 

( f) 

df 

3 Experiment Method 

The experiment was involving two professional wall climbing athletes as the participants. At 

the beginning, subjects were asked to perform three times VC (Voluntary Contraction) with 

handgrip dynamometer by right hand. The maximum value from three measurements was 

taken as handgrip strength value. Then, handgrip endurance value would be determined 

from the period of time to endure gripping force, as 70 percents of handgrip strength value. 

When performed gripping activity, EMG signals was recorded from finger flexor and finger 

extensor muscles. The locations of these two muscles were shown in figure 2. Then, power 

spectral of these EMG signals would be analyzed by using algorithm on Matlab 7.0. 

Measurements of handgrip strength, handgrip endurance, and EMG were performed four 

times, which were which were before and soon after climbing, after first 15 minutes and 

after the second 15 minutes active recovery process. For lactate acid level, measurements 

were performed twice, which were before and soon after climb activity. The measurement 

was performed in the use of lactate analyzer by taking blood sample from auricle. The 

athlete will be ordered to climb the boulder for 15 minutes with a certain difficulty level. 

4 Results 

Figure 2. Measurements of Handgrip parameters and recording of EMG Signals 

The results of EMG signals spectral analysis would be compared with handgrip endurance 

parameter and lactate acid accumulation. In this case, handgrip endurance parameter and 

lactate acid accumulation are considered as reference to determine muscle fatigue level. 

(2)


On subject 1, handgrip endurance value decreased after climbing and the first active 

recovery period, it then increased after the second active recovery period. The lactate acid 

accumulation increased after climbing. On finger extensor muscle, PSD value tended to 

increase and MF value tended to decrease when handgrip endurance measurement was 

performed. However, it happened vice versa on finger flexor muscle. 

Figure 3. Handgrip endurance and lactate acid accumulation on subject 1 

Figure 4. Handgrip endurance and lactate acid accumulation on subject 2 

Figure 5. PSD on subject 1 and its linearization 

45


Figure 6. Median frequency on subject 1 and its linearization 

On subject 2, handgrip endurance value decreased after climbing, it then increased after 

the first active recovery period with the nearly equal value after the second active recovery 

period. The lactate acid accumulation increased after climbing, with a higher increase than 

subject 1. On finger flexor muscle, PSD value tended to increase and MF value tended to 

decrease when handgrip endurance measurement was performed. While, on finger 

extensor muscle, PSD value and MF value tended to decrease. 

Figure 7. PSD on subject 2 and its linearization 

Then, it was performed two ways ANOVA statistical analysis, with the source of variations, 

consist of data collecting session and subject. Recapitulations of the results of ANOVA 

analysis were shown on table 1 and 2. A variation source would be influenced significantly, 

if Fcrit value was less than F value. From these results, subject parameter influenced PSD 

significantly only on finger extensor muscle when handgrip endurance measurement was 

performed. 

46


Figure 8. Median frequency on subject 1 and its linearization 

Table 1. ANOVA table (source of variation : session) 

Table 2. ANOVA table (source of variation : subject) 

47


5 Conclusions 

1. The increase of muscle fatigue was shown by tendency of PSD increase and MF 

decrease, correlate with handgrip endurance decrease and lactate acid increase. 

2. The muscles that experienced higher fatigue level when gripping were different one to 

another subject. Subject 1 (finger extensor), subject 2 (finger flexor). 

3. Subject parameter influenced PSD significantly only on finger extensor muscle when 

handgrip endurance measurement was performed. 

4. Generally, active recovery period gives positive influence to recover muscle condition, 

from handgrip endurance parameter. 

6 Reference 

[1] Tarata, Mihai T., “Mechanomyography versus Electromyography, in Monitoring The 

Muscular Fatigue”, Biomedical Engineering Online, February 2003. 

[2] “Sistem Muskular,” class notes, Sekolah Teknik Elektro dan Informatika, Institut 

Teknologi Bandung, Bandung, Indonesia, 2007. 

[3] De Luca, C.J., “Surface Electromyography: Detection and Recording”, DelSys 

Incorporated, 2002. 

[4] De Luca, C.J., “The Use of Surface Electromyography in Biomechanics”, Journal of 

Applied Biomechanics, pp. 13(2): 135-163, 1997. 

[5] De Luca, C.J.,”Electromyography in Encyclopedia of Medical Devices and 

Instrumentation (John G. Webster, Ed.)”, USA: John Wiley Publisher, 2006, pp. 98-109. 

[6] De Luca, Gianluca, “Fundamental Concepts in EMG Signal Acquisition”, DelSys 

Incorporated, 2001. 

[7] Susanti, Hesty, “Analisis Sinyal Respon Electromyography terhadap Stimulasi Terapi 

Akupuntur” Final Project, Institut Teknologi Bandung, 2008. 

[8] Muttaqien, Sjaikhunnas El, “Pengembangan Sistem Untuk Mengevaluasi Performansi 

Otot Pada Genggaman Tangan” Final Project, Institut Teknologi Bandung, Bandung, 

Indonesia, 2009. 

[9] Tjokronegoro, Harijono A.,” Analisis Spektral Digital”, Indonesia: Penerbit ITB, 2004. 

[10] Tjokronegoro, Harijono A.,”Pengolahan Sinyal”, Indonesia: Penerbit ITB, 2005. 

[11] DR. Sugiono, “Statistika untuk Penelitian”, Indonesia: Alfabeta Bandung, 2002. 

48


Study on Voltage Controller of Self-Excited Induction Generator 

Using Controlled Shunt Capacitor, SVC Magnetic Energy Recovery 

Switch 

Abstract 

F.D. Wijaya, T. Isobe, R. Shimada 

Tokyo Institute of Technology, 

2-12-1 Ookayama Tokyo 152-8550 Japan 

Reactive compensation is required to maintain terminal voltage of induction generator under varying 

load and speed operation. A new variable shunt capacitor, which is called SVC magnetic energy 

recovery switch (SVC MERS), is proposed. The operation principle, characteristics of injected current, 

operating range of reactive compensation of SVC MERS in star and delta configuration were 

investigated. Application for induction generator voltage controller, which is required leading reactive 

compensator, is suitable for SVC MERS. Small scale experiments were conducted to verify the 

proposed system performance to control induction generator voltage in variable load and speed 

conditions. The advantage of this device is simple control with low switching frequency. Moreover in 

delta configuration, the SVC MERS current is low means downsizing of heatsink can be achieved. 

Keywords : Voltage controller, induction generator, reactive compensation, SVC MERS 


The global warming issue as well as the fossil fuel limitation has made human kind to do 

more research in the area of renewable energy sources. One of the promising research area 

is application of self-excited induction generator (SEIG) in micro-hydro power, wind power 

and diesel engine with bio-fuel. For example, in Indonesia as energy supply became a 

problem, the government projected 500 MW of micro-hydro to be installed, especially in 

rural areas to develop a green source power system [1]. Such a system would normally be 

operated as an isolated system supplying electricity to local un-electrified areas because it 

can save transmission and distribution capital investment cost. Some advantages can be 

achieved such as CO2 reduction and environmental awareness. 

SEIG consists of an ordinary three phase induction machine excited by a bank of capacitors 

and driven by a prime mover, such as hydro turbine, wind turbine, flywheel system or diesel 

engine [2]. Low cost, robustness and low maintenance need are some of the reasons to use 

this machine. 

However, there is a problem in the operation of SEIG, with poor voltage regulation in varying 

load conditions. Various approaches have been proposed for overcoming these problems. 

Availability of low cost controllable power devices, such as IGBTs, have made the application 

of power electronic based VAR compensation possible. Various controllable reactive power 

supplies exist such as TSC (Thyristor Switched Capacitor), TSC-TCR (Thyristor Controlled 

Reactor), STATCOM, and other variable shunt compensators [3-6]. TSC can only give a 

variation of capacitance in discrete steps. In transients conditions, charging and discharging 

of the capacitor will stress the thyristor. To avoid these problems, a combination of TCR and 

TSC is developed. It has a large reactor in the TCR, in order to have a large continuous 

control range. The latest technology is STATCOM, which uses PWM inverter as voltage source 

49


with high frequency switching to reduce harmonic and more complex control is required 

[4,5]. 

In this paper, SVC MERS is proposed to control the voltage of induction generator with low 

switching and simple control using voltage feedback with PI controller. 

2 Induction Generator Characteristics 

To generate rated voltage of induction generator, VAR compensation is required. If the 

induction generator is connected to the grid, VAR can be supplied from the grid by other 

reactive power sources, such as synchronous generator. In isolated or stand-alone 

condition, capacitor is usually used. This system is called SEIG. 

Self excitation of the generator begins by the action of either a residual magnetism of the 

iron core and charge in the excitation capacitors. When the induction machine is driven by a 

prime mover, the residual magnetism of the iron core associated with an external capacitor 

that generates current by rotor movement inside of this magnetic field will produce induces 

voltages in the stator windings at a frequency proportional to the rotor speed. However, if 

there is no residual magnetism, induction generator voltage cannot be generated. 

A variable capacitor is required in order to realize voltage regulation of SEIG in varying load 

conditions or for variable speeds. From theoretical calculations [7], a range of fixed shunt 

capacitor sizes can be calculated to maintain rated voltage under load varying conditions. 

Fig. 1 shows load characteristic curve for a range of fixed capacitor sizes at synchronous 

speed and unity power factor load for a 200V 1.5 kW induction machine with magnetizing 

curve given in Eqn. (1) and induction generator parameter shown in Table 1. 

Lm= 0.6778Im 3 – 7.9931Im 2 + 16.231Im + 115.04 (1) 

The curve in Fig. 1 shows that variable compensation is needed to maintain rated voltage. 

Higher capacitance is required if load has low power factor as shown in Fig. 2. 

Voltage (Volt) 

200 

150 

100 

160uF 

150uF 

140uF 

130uF 

120uF 

110uF 

0 500 1000 1500 

Output Power (Watt) 

Figure 1. Effect of Excitation 

Capacitorof 1.5 kW 200 V Induction 

Generator. 

50 

Excitation capacitor ( μF) 

300 

240 

180 

120 

0 500 1000 1500 

Output power (W) 

pf=0.8 

pf=0.9 

pf=1.0 

Figure 2. Required excitation capacitor of 

SEIG for Inductive Load at Constant 

Voltage and Speed.


Table 1. Parameters of induction generator 

Induction generator 1.5 kW, 200V, 6.8 A, 

50 Hz 

- Stator resistance, Rs 1.337 Ω 

- Rotor resistance, Rr 0.713 Ω 

- Stator & rotor 

inductance, Ls, Lr 

3.85 mH 

3 SVC Magnetic Energy Recovery Switch 

3.1 Star Configuration 

The configuration of this device is based on 4 IGBTs and a dc capacitor per phase. It is 

called magnetic energy recovery switch (MERS), and typically inserted in series between AC 

source and load, as series reactive compensation applied for power factor correction and 

power flow control [7,8,9]. In this paper, this device is used as shunt reactive compensation 

as shown in Fig.3. MERS is connected with an inductor in series as a filter to reduce the 

harmonic current flowing in the system and then it is called SVC MERS. 

3.1.1 Operational Principle 

Figure 3. Configuration of SVC MERS in Star Connection 

Operational states to control the injected current are shown in Fig.4. Two IGBTs are turned 

on and off in pairs one time each cycle of the ac power source (50 Hz) and controlled 

synchronously. In a half cycle, two switches (S1 and S3) are turned on, the current flowing 

is charging and discharging the dc capacitor with the same polarity. When the dc capacitor 

voltage is equal to zero, the current is flowing in parallel. The other half cycle, the other pair 

(S2 and S4) is turned on, with similar conditions, but with the opposite current flow direction 

The waveforms of phase voltage, shunt current, dc capacitor voltage, gate signal and IGBT 

current are shown in Fig. 5. It can be seen that the IGBT always turn on at zero current and 

turn off at zero voltage, therefore the low switching losses can be achieved. By controlling 

the switches as describe above, three different control can be achieved, which are balance 

mode when Xc = Xmers, dc-offset mode when Xmers > Xc and discontinuous mode when Xmers 

< Xc, where Xc is MERS capacitance and Xmers is variable capacitance. In the dc-offset 

mode, the IGBT will turn off at non zero voltage. This is because a small voltage still remain 

in the dc capacitor. For three phase systems there will be one SVC MERS per phase. 

51


Source voltage (pu) 

MERS current (pu) 

MERS voltage (pu) 

Figure 4. The operational state condition of SVC MERS 

Gate 2, 4 

1 

0 

−1 

20 

0 

−20 

4 

0 

−4 

1 

dc−offset 

0 

0 0.01 

Time (s) 

0.02 

Figure 5. Typical waveforms of SVC MERS balance mode, discontinuous mode and dc-offset 

mode 

52 

balance 

discontinuous 

δ 0 

δ 1 

δ 2


Variable reactive compensation can be achieved by controlling the current flowing to the dc 

capacitor by applying appropriate gate signals. The control is based on performing a phase 

shift of the gate signals. The control variable called δ phase, which is the phase difference 

between phase voltage (Vln) and the time of switching. The value of δ phase depends on how 

much reactive power must be supplied to the induction generator and the load. 

From other point of view, as illustrated in Fig. 6, SVC MERS is a capacitor controlled by 

semiconductor devices. The reactive/shunt current Isvc mers and the reactive power Qsvc mers, 

can be represented as follows: 

⎛ ⎞ 

⎜ Vin 

I ⎟ 

svc mers = 

(2) 

⎜ ⎟ 

⎝ 

X svc mers ⎠ 

⎛ 2 ⎞ 

⎜ Vin 

Q = ⎟ 

svc mers ⎜ ⎟ 

⎝ 

X svc mers ⎠ 

(3) 

X = X (δ ) − X 

(4) 

svc mers 

53 

( ) 

mers 

Figure 6. Equivalent circuit of SVC MERS 

3.1.2 Control System 

In order to control the terminal voltage, voltage feedback with PI control is proposed as 

shown in Fig. 7. The control part starts with sensing the line to line voltage. Only two voltage 

sensors are used, which are fed to the control board. Phase lock loop (PLL) technique is 

applied to synchronize the gate switching time to the phase of the line voltage. However, 

zero detection can also be used to make this synchronization. However zero crossing 

detection of voltage can also be used for synchronization. 

For feedback control, the rms value of the line voltage is compared to the reference voltage. 

The error is given to the PI controller to determine the δ phase, and then it is fed to the gate 

controller to generate the gate signals. A δ phase limiter is to keep δ phase in the operating 

area. 

L


Figure 7. Control system of SVC MERS 

3.1.3 Characteristics of Injected Current 

The characteristic of the injected current to the system is determined by the size of the 

capacitor and inductor. The selection of the operating range can be based on Fig.1. The 

minimum injected current should be equal to the magnetizing current of the induction 

generator to generate rated voltage at no load condition. 

Fig.8 shows the relationship of the injected current to δ phase and the relative reactance. 

The relative reactance is the ratio of the equivalent reactance Xsvc mers to actual reactance 

XC-XL . 

Relative reactance 

1.1 

1 

0.9 

0.8 

dc−offset 

X eq 

discontinuous 

balance 

−10 0 10 20 

δ (deg) 

I injected 

Figure 8. Characteristics of the injected 

current with 110uF and 10mH 

1.1 

1 

0.9 

0.8 

Current (pu) 

54 


Noload Fulload 

4 

3 

2 

1 

0 

balance 

X eq 

Over load for resistive load 

Resistive load Inductive load at 1 pu load (pf low) 

Low speed operation 

0 30 60 90 

δ (deg) 

I injected 

4 

3 

2 

1 

0 

Current (pu) 

Figure 9. Operating range area of SVC MERS for 

SEIG at rated voltage 

The operating range availability of SVC MERS to compensate reactive power of induction 

generator is shown in Fig.9. In the induction generation operation point, more reactive 

power is required to supply in inductive load or low speed operation


The injected current contains some harmonics; therefore inductor must be inserted as a 

filter. The relationship between the inductance and the harmonic of the injected current 

for various operating points is shown in Fig.10. Three combinations of capacitor and 

inductor were simulated. It can be seen that higher inductance will reduce the harmonic 

of the injected current; on the other hand the capacitance can be reduced. 

THD current (%) 

8 

6 

4 

2 

not−continuous dc−offset 

C=110uF; L=10mH 

C=105uF; L=15mH 

C=100uF; L=20mH 

0 

0.8 0.9 1 1.1 


Figure 10. Harmonic of the injected current 

3.1.4 Steady state and transient characteristics 

The experimental data results in presented in pu which base voltage is 200 V, base current 

6.8 A and base speed is 1500 rpm. Fig 11 and 12 show steady state voltage and current 

characteristics of the system.At no load condition, SVC MERS is supplied reactive current at 

about 0.56 pu in order to generate rated voltage. The reactive current represented by shunt 

current increased as load increased and terminal voltage always maintaned constant in 

load varying conditions. 

Output power (pu) 

0.8 

0.6 

0.4 

0.2 

Output power 

Terminal voltage 

0 

−10 0 

Phase shift angle δ (deg ) 

10 

55 

Balance point 

Figure 11. Steady state voltage characteristics at load varying conditions 

1 

0.5 

0 

Voltage (pu)


Current (pu) 

1 

0.5 

Stator 

Load 

0 

0 0.2 0.4 0.6 

Output power (pu) 

56 

Shunt 

Figure 12. Steady state current characteristics 

Fig. 13 shows the experimental transient response of the voltage, load current, shunt 

current, and dc capacitor voltage with a step change from no load to full resistive load 

condition. The voltage is recovered within two cycles. Operation changed from dc-offset 

mode to discontinuous mode in order to supply required reactive power. 

Terminal voltage (V) 

Current (A) 

DC Capacitor voltage (V) 

200 

0 

−200 

10 

0 

−10 

200 

100 

0 

Shunt 

0 time (s) 0.1 

Load 

Figure 13. Transient characteristics of the system


Variable speed condition was also experimented. The results is shown in Fig. 14. In this 

experiment, the rotor speed is changed from 0.8 pu to 1.3 pu, while the induction generator 

output power was setting at half of its rated power (750 W). It can be found that SVC MERS 

can control the voltage to its rated voltage keeping the induction generator to generate 

output power. 

For higher speed, the value of phase shift angle δ is small, meaning low reactive power 

is generated by SVC MERS. While for lower speed, phase shift angle δ is larger, meaning 

higher reactive power is required to maintain its rated voltage. 

Speed (pu) 

1.2 

1 

0.8 

Terminal voltage 

Speed 

0 16 32 

0 

48 

Phase shift angle δ (deg) 

57 

1 

0.5 

Figure 14. Variable speed characteristic using SVC MERS 

3.2 Delta Configuration 

In order to reduce the current rating of the switch, delta connection is configured for this 

application, however capacity rating will similar. By doing this, the capacitance can be 

reduced to 37 µF (1/3 of star). The control of the switch for MERS is the same as star 

configuration. Fig. 15 shows the waveforms of terminal voltage, shunt current, MERS 

current, stator current, IGBT and gate signal of this configuration at 0.7 pu load. In half 

cycle capacitor will charge (a), dis-charge (b) and have parallel path (c). It can be found 

that MERS current is smaller than shunt current. As a result IGBT losses will be lower, 

therefore downsizing of heat sink can be achieved. 

4 Conclusion 

The operation principle, characteristics of injected current, operating range of reactive 

compensation of SVC MERS in star and delta configuration was investigated. Application for 

induction generator voltage control, which is required leading reactive compensator, is 

suitable for the proposed system. Experimental result confirmed that the proposed system 

can controlled induction generator in load and speed varying conditions. This proposed 

system has the following advantages: i) simple control, where only two voltage sensors are 

required and voltage feedback control with PI controller gives a good response, ii) low 

Voltage (pu)


switching frequency, which zero current turn on and zero voltage turn off and resulting in 

low switching losses. 

5 References 

Terminal voltage(V) 

Current (A) 

Capacitor voltage (V) 

Current (A) 

200 

0 

−200 

10 

0 

−10 

300 

200 

100 

0 

4 

0 

−4 

(a) (b) 

MERS 

0 0.01 0.02 

Time (s) 

0.03 

58 

(c) 

shunt 

Gate S2−S4 

stator 

Figure 15. Waveforms of SVC MERS in delta configuration 

[1] UNDP Report: 

"Connecting micro-hydro power Indonesia to the national grid", UNDP Report (2003) 

[2] E.D. Basset, F.M. Potter, “Capacitive excitation for induction generator”,AIEE Trans. 

pp.540–73., May 1935. 

[3] T. Ahmed, O. Noro, E. Hiraki, M. Nakaoka, “Terminal voltage regulation characteristics 

by static var compensator for a three-phase self-excited induction generator”, IEEE 

Trans. on IEEE Industry Applications”, Vol. 40, No. 4, July 2004. 

[4] Mustafa, A. Al-Saffar, Eui-Cheol N., T. A. Lipo, “Controlled shunt capacitor self-excited 

induction generator,” Proc.33-rd IAS IEEE annual meetings USA, 1989. 

[5] R. Leidhold, G. Garcia, M. I. Valla, “Induction generator controller based on the 

instantaneous reactive power theory”, IEEE Trans. on Energy Conversion, vol. 17, no. 3. 

pp. 368-373 September 2002. 

4 

0 

−4 

Gate voltage (V)


[6] M. Naidu and J.Walters, “A 4-kW 42-V induction-machine-based automotive power 

generation system with a diode bridge rectifier and a PWM inverter,” IEEE Trans. Ind. 

Applicat., vol. 39, pp. 1287–1293, September 2003. 

[7] T.F. Chan, “Analysis of self-excited induction generators using an iterative method”, 

IEEE Trans. Energy Conversion. vol. 10, pp. 502-507, September 1995. 

[8] T. Isobe, J.A. Wiik, F. Danang Wijaya, K. Inoue, K. Usuki, T. Kitahara, R. Shimada,” 

Improved performance of induction motor using magnetic energy recovery switch”, 

presented at the 4th PCC Nagoya, May 2007. 

[9] J.A. Wiik, F. Danang Wijaya, R. Shimada,” An innovative series connected power flow 

controller, Magnetic Energy Recovery Switch (MERS)”, presented at IEEE PES General 

Meeting USA July 2007. 

[10] J.A. Wiik, T. Isobe, T. Takaku, T. Kitahara, R. Shimada, “Reactive power compensation by 

using series connected current phase control switches”, presented at PCIM China, 

2006. 

59

Jurnal Otomasi, Kontrol & Instrumentasi 

Informasi bagi Penulis Naskah 

Penerimaan Naskah 

Naskah yang diterima berasal dari civitas academica baik dari Institut Teknologi Bandung maupun 

dari luar ITB, dan dari kalangan industri yang berhubungan dalam bidang otomasi, kontrol, dan 

instrumentsi. Naskah yang dikirim tidak pernah dipublikasikan pada jurnal atau majalah yang lain 

sebelumnya. Bahasa yang dipakai dalam penulisan adalah bahasa Indonesia atau bahasa Inggris. 

Naskah akan diuji kelayakannya melalui sistem refereeing oleh para penilai berdasarkan keaslian 

(orisinalitas), relevansi, dan validitas ilmiah. 

Penulisan Naskah 

Format penulisan dan jenis font mengikuti petunjuk berikut ini : 

Mirror margin, margin dalam 2.5 cm, margin luar 2 cm, margin atas dan bawah 2.54 cm. 

Ukuran judul 14 pt (bold), nama penulis 10 pt, alamat institusi 10 pt. 

Abstrak dan kata kunci 9 pt, judul bab / sub bab 12 / 10 pt, body text 10 pt. 

Judul gambar/tabel 9 pt (bold), ukuran kertas : B5 (JIS), jenis huruf : Franklin Gothic Book. 

Contoh penulisan judul dan penulis : 

A Measurement-Based Form of the Out-of-Place Quantum Carry 

Lookhead Adder 

A. Trisetyarso1) , R. Van Meter1) , K. M. Itoh2) 1) Department of Applied Physics and Physico-Informatics, KeioUniversity, Japan 

2) Yagami Campus, 3-14-1Hiyoshi, Kohoku-ku,Yokohama-shi, Kanagawa-ken 223-8522, 

Japan 

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Penulisan abstrak untuk naskah ditulis dalam bahasa Indonesia atau bahasa Inggris disesuaikan 

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[1] J.F. MacGregor, D.T. Fogal, Judul jurnal, Nama jurnal, 5 (3) (1995) hal 163-171. 

[2] J.F. MacGregor, D.T. Fogal, Judul buku, ed.2, Penerbit (1997), hal 5-10. 

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