Comparison between class A And meter PQM

Voltage Sags and Total Harmonic Distortion
Monitoring in Power Systems. A case study
Niculai STANCIU 1 , Dorel STĂNESCU 2 , Petru POSTOLACHE 3 , Willibald SZABO 4
1 S.C. FDEE Electrica Distribuţie Transilvania Sud S.A., Brasov, ROMANIA, niculai_stanciu@yahoo.com
2 S.C. FDEE Electrica Distribuţie Transilvania Sud S.A., Brasov, ROMANIA, dorel.stanescu@electricats.ro
3 University „POLITEHNICA” of Bucharest, ROMANIA, petrupostolache@yahoo.com
4 University “TRANSILVANIA” Brasov, ROMANIA, w_l_szabo@yahoo.co.uk
Abstract: The purpose of Power Quality (PQ) measurements
can be either continuous “surveillance” as a task performed
by the Distribution System Operator (DSO) or specific parameter
“evaluation” for limit violation. Individual strategies
can be applied for each goal but a combined approach
is preferred.
This paper presents a comparison between results obtained
using an electricity meter with PQ capabilities (surveillance
task) and a class A PQ monitoring equipment (used to evaluate
limit violation determination).
Measurement uncertainty was calculated for each device
and measurand and the result was compared with IEC
61000-4-30 requirements.
KEYWORDS: power quality, voltage sags, Total Harmonic
Distortion, measurement uncertainty
I. INTRODUCTION
Monitoring Power Quality parameter is one of the
DSO’s constant concerns. Improving the service quality
when the number of power electronics applications is increasing
must be accompanied with a continuous preoccupation
for finding new means to identify and evaluate the
PQ problems. Preventions measures may then be taken.
PQ parameter evaluation is used to determine limit violations.
Measurements must have a proper accuracy in
order to obtain efficient correction measures.
PQ parameters surveillance and evaluation are actually
two complementary stages of the same process of providing
the customer with high quality services [1].
II. MEASURING AND MONITORING EQUIPMENT
Nowadays, measurement technology is capable of very
fast acquisition and processing and high accuracy.
Systems for acquisition, management and data storage
can record signals corresponding to phenomena that affect
electric networks over relevant periods of time.
The equipment for measuring/monitoring PQ parameters
is classified in compliance with IEC 61000-4-30, [2],
standard that is correlated with measuring methods/procedures
and accuracy level:
• Class A instruments;
• Class S instruments;
• Class B instruments.
The metrological and technical conditions for each
class, as well as the use of the results, are regulated by the
same standard and connected standards IEC 61000-4-7,
[3], 61000-4-15, [4], and IEC 61557-12, [5].
The „Necessary Condition” is fulfilled when an instrument
included in one of the classes above is available.
In the measurement process the “Sufficient Condition”
must be proven. This means that the methods, procedures
and recommendations must be correctly applied. We will
try to demonstrate this below.
Proper measuring and monitoring instruments will be
selected depending on the actual purpose.
Choosing metering / monitoring equipment takes into
account the technical and economical analyses. First of
all, the technical and metrological requirements must be
fulfilled and then the initial investment and maintenance
costs must be considered.
The content of „Technical Specification” is very important
during this kind of analysis because the lack of
relevant information can influence the correct decision,
[6].
III. TECHNICAL SPECIFICATIONS FOR MEASURING
INSTRUMENTS
Technical specification for measuring/monitoring instruments
of PQ parameters includes information for static
and dynamic performance characteristics. It is important
to remember that those characteristics refer to:
• Performance of the measuring instrument for
each characteristic;
• Time intervals recommended for instrument recalibration
to confirm metrological status;
• Probability of measurement performance.
Important: If the tolerated limits and probability distribution
are provided in the Technical Specification for
some errors, then the measurement uncertainty can be
estimated, [7]. While measuring uncertainty evaluation, it
is very recommended that the Technical Specification
should make references to basic probability distribution
used to establish tolerance limits for each performance
parameter.
Evaluation of static and dynamic characteristics of
measuring instruments is required at certain time intervals
(no longer than the time interval specified by manufacturer).
This process of recalibration ensures that those
characteristics stay within the specified tolerance limits.
On these occasions the systematic errors and associate
uncertainty are determined. Then, the risk analysis is performed
to check whether the instrument is outside imposed
tolerance, [8].
In this way, the measurement results characterize phenomena
with high objectivity.

IV. MEASUREMENT UNCERTAINTY
Every measurement is accompanied by errors associated
with the measuring equipment, environmental conditions
during measurements as well as the methods /
procedures used. Because systems that get involved in
measuring process cannot ensure perfect reliability the
errors can also vary. In most cases phenomena can be
controlled only to a certain extent, with a certain probability.
For this reason the deviation of measuring error as
magnitude and sign is named „measurement uncertainty”.
A. Measurement uncertainty estimation based on
technical specification, before actual measurements
In this case, it is important the user knows that some
technical specifications are established by testing a selected
sample from mass production of that model. Because
the results are applied to the whole population, limits
are stated to ensure that the majority would comply.
Thus, we deal with a certain confidence level and degrees
of freedom depending on the sample size. Tolerance limits
for performance characteristic, „x”, will be written as:
± L = ± tα / 2,
ν ⋅ s x
(1)
where:
t = t – statistic coefficient;
α / 2,ν
α =relevance level = 1 – C/100;
C = confidence level, [%];
ν = degrees of freedom = n-1;
n = sample size;
s
x
= sample standard deviation.
Uncertainty, u, related to a characteristic with
normal distribution of error variation, estimated to the
tolerated error limits, ± L, confidence level, p, normal
−1
inverse cumulative distribution function, Φ (.)
, is:
L
u =
(2)
−1⎛ 1+
p ⎞
Φ ⎜ ⎟
⎝ 2 ⎠
Uncertainty estimation using technical specifications
facilitates the adequate choice of measuring instrument.
B. Measurement uncertainty estimation after actual
measurements
Uncertainty evaluation methodology used after actual
measurement ensures proper statement of measurement
result as close as possible to the real value.
Following the recommendations of standard ISO/IEC
98-3, i.e. „Evaluation of measurement data — Guide to
the expression of uncertainty in measurement”, [9], the
measurement uncertainty can be determined for each
measured parameter taking into account the mathematical
model for variation law and nature of errors in measurement
process.
In this way, the measurement results characterize phenomena
with high objectivity, with probability of 95%.
V. EXPERIMENTAL RESULTS
Voltage sags and Total Harmonics Distortion for voltage
and current are most common PQ parameters and, as
a consequence, measurements in “surveillance” and
“evaluation” took them into account.
For this purpose we used:
• Energy static meter A1RLQ+, SN: 2748989 for
surveillance;
• PQ Analyzer Fluke 435, SN: N10140 for evaluation.
Electrical signals were generated using C300 Calmet
Three phase Calibrator, SN: CT/260/2011.
Metrologic trace is ensured by making use of the following
documents: Calibration Certificate for C300 Calemt
Calibrator, [10], and Fluke 435, [11], and Metrological
Certificate, [12], for Energy static meter A1RLQ+.
A. Voltage sags measurement/ monitoring
Predefined voltage residual values for voltage sags are
presented in Table 1.
Table 1
Inferior
limit of
U res As in IEC 61000-4-30
supply
voltage [V] % din U din Class
0,9U din 207,00 0 A S B
206,77 -0,10%
206,54 -0,20%
204,70 -1%
202,40 -2%
Voltage levels generated with C300 Calmet Calibrator,
SN: CT / 260 / 2011 are indicated in Table 2. Test performed
on 03.12.2012.
Table 2
Time U 1 U 2 U 3
[hh:mm:ss] [V] [V] [V]
17:39:13 230,00 230,00 230,00
17:39:13 207,00 207,00 207,00
17:39:13 230,00 230,00 230,00
17:39:13 206,77 206,77 206,77
17:39:13 230,00 230,00 230,00
17:39:13 206,54 206,54 206,54
17:39:13 230,00 230,00 230,00
17:39:13 206,54 206,54 206,54
17:39:13 230,00 230,00 230,00
17:39:13 204,70 204,70 204,70
17:39:13 230,00 230,00 230,00
17:39:13 202,40 202,40 202,40
17:39:13 230,00 230,00 230,00
Table 3 shows the recordings of voltage sags measuring
/ monitoring using PQ Analyzer Fluke 435, SN:
N10140. Test performed on 03.12.2102.
Table 3

Time
Event
Residual
voltage
[hh:mm:ss] Type Duration [V]
17:39:56 650ms Sag 0m.1s.426ms. 206,95
17:39:59 915ms Sag 0m.1s.576ms. 206,71
17:40:03 854ms Sag 0m.1s.637ms. 206,48
17:40:07 854ms Sag 0m.1s.637ms. 206,49
17:40:11 713ms Sag 0m.1s.787ms. 204,65
17:40:15 653ms Sag 0m.1s.856ms. 202,35
Table 4 presents the recordings of voltage sags measuring
/ monitoring using Energy static meter A1RLQ+, SN:
2748989. Test performed on 03.12.2012.
Table 4
Time
Event
[hh:mm:ss]
17:38:29
Started sag [7202]
1
17:38:32 End sag [7203]
17:38:33
Started sag [7202]
2
17:38:36 End sag [7203]
17:38:37
Started sag [7202]
3
17:38:40 End sag [7203]
17:38:41
Started sag [7202]
4
17:38:44 End sag [7203]
17:38:45
Started sag [7202]
5
17:38:48 End sag [7203]
17:38:49
Started sag [7202]
6
17:38:53 End sag [7203]
B. Measuring / monitoring of voltage and current
distorted waveforms
Using the C300 Calmet Calibrator several three phase
distorted signals were generated:
• three phase distorted voltage waveforms with
THD value of 10%;
• three phase distorted current waveforms with
THD value of 50%;
Figures 1, a)... d) present screen captures and values
measured with Fluke 435 PQ analyzer for voltage waveforms.
Fig. 1, b) Three phase voltage waveforms
Fig. 1, c) Harmonics spectrum
Fig. 1, d) Voltage THD and individual harmonics values
Test performed on 20.11.2011 during the time interval
18:23 - 18:50.
Voltage THD values per phase recorded with Energy
static meter A1RLQ+, with SN: 2748989, are presented
in Table 5. Test performed on 20.11.2012.
Fig. 1, a) Voltage RMS values and phasors for voltage

Table 5
THD THD THD
Time
phase L 1 phase L 2 phase L 3
18:47 9,8 10,0 9,9
Figures 2, a) ... d) present screen captures and values
measured with PQ Analyzer FLUKE 435 for current
waveforms.
Fig. 2, d) Current THD and individual harmonics values
Fig. 2, a) Current RMS values and phasors for voltage and current
Fig. 2, b) Three phase current waveforms
Fig. 2, c) Harmonics spectrum
Test performed on 20.11.2011 during the time interval
18:23 to 18:50.
Current THD values per phase recorded with Energy
static meter A1RLQ+, with SN: 2748989, are presented
in Table 6.
Table 6
THD THD THD
Ora
phase L 1 phase L 2 phase L 3
18:47 49,8 48,8 48,9
Residual voltage measurement uncertainty for voltage
sags detected by Energy static meter A1RLQ+ was estimated
using information from technical specification,
[13], and formula (2):
2,3
U = = 1, 02 V
2,248
where:
• 2,3 V represents the limit value L = ± 1% from
voltage reference value U ref = 230 V, as in IEC
62053-22:2003, [14];
• 2,248 is the value of normal inverse cumulative
−1
distribution function Φ (.)
, for 95% probability,
assigned for limit L.
We can state that voltage sags detected by Energy static
meter A1RLQ+ have a measurement uncertainty of 1V
with a 95% probability.
The residual voltage measurement uncertainty for voltage
sags detected measured with PQ Analyzer Fluke 435
was estimated using recommendations included in Guide
ISO/IEC 98-3, [9], with formula:
U = k ⋅u
n
2
c
= k ⋅ ∑u i
i=
1
(3)
where:
• k multiplier of the combined uncertainty; k = 2
when the real value falls within ± U interval with
95% probability;
• u c composed standard uncertainty;
• u i components of standard uncertainty allocated to
different error types.

Measurements were performed in reference conditions
with ambient temperature inside (21,7 ... 23,2) ºC interval
and humidity inside (47,6 ... 58,9)% interval. In this case
only following components have significant contributions:
• Instrument uncertainty stated by Calibration Certificate
no. 1287, [11]:
U
PMD
0,05 V
uδ
PMD
= = = 0, 025V
k 2
• Hysteresis uncertainty:
Vd
−Vc
0,01V
uδ
H
= = = 0, 00577V
3 3
• Resolution uncertainty:
0,5d
0,5 ⋅ 0,01V
uδ
rez
= = = 0, 00288V
3 3
Note: 10 measurements were performed under
repeatability conditions for each Ures value. The
experimental values measured with the network analyzer
FLUKE 435 were identical, as they were generated with
Calibrator Calmet C300, which has a high level of
accuracy. In this case the standard deviation is zero. The
standard uncertainty of type A is implicitely null.
So, composed uncertainty calculated according “uncertainty
propagation law” [9], is:
2 2 2
uc = uδ
PMD
+ uδH
+ uδrez
= 0, 0258V
Extended uncertainty according formula (3) is:
U = 2 ⋅ 0,0258V
= 0,0516 V ≅ 0, 05V
In case of THD measurements, technical specification,
[15], or calibration certificate, [10], do not include tolerance
limits or uncertainty value.
For this reason, uncertainty cannot be calculated.
Instead, we compared the measured values against prescribed
values:
• maximal measuring error for Energy static meter
A1RLQ+ :
o 2% for distorted voltage
o 2,4% for distorted current
• maximal measuring error for PQ Analyser Fluke
435 :
o 2% for distorted voltage
o 1,8% for distorted current
As far as measurement accuracy for THD is concerned,
both instruments fall within limit of 5% for Class I
equipments stated by IEC 61000-4-7:2003, Electromagnetic
compatibility guide, [3].
VI. CONCLUSIONS
The goal of experimental measurements was to identify
and propose an adequate strategy for choice of instruments
used for measuring/monitoring PQ parameters.
Perturbations simulated using Calmet C300, SN: CT /
260 / 2011 calibrator were measured / monitored correctly
with both Energy static meter A1RLQ+ and PQ
Analyzer Fluke 435:
a) voltage sags;
b) distorted waveforms for voltage and current with
predefined harmonics spectrum and THD.
This means that:
• sag threshold settings were correct;
• correct settings were possible because the uncertainty
was known;
• the necessary condition is fulfilled in order to develop
a sag monitoring strategy based on modeling
software for choosing optimal location of PQ
enabled electronic energy meters and/or PQ network
analyzers.
Both types of PMD can be used for scheduling permanent
and/or pre-determined measurements/monitoring of
distorted waveforms of voltage and current. Thus, based
on these measurements/monitoring, one can highlight
distortions between “starting time” (initiation of distortion
event) and “inhibition time” (end of the event), [17].
We can then choose to use the Energy static meter with
PQ capabilities for parameter surveillance, or in case of
further investigation, PQ analyzers must be employed.
REFERENCES
[1] Golovanov, I.C., Measurement of electrical quantities in
power system, Romanian Academy and AGIR Ed.
Bucharest, 2009
[2] ***Electromagnetic compatibility (EMC). Part 4-30:
Testing and measurement techniques. Power quality measurement
methods, IEC 61000-4-30
[3] ***Electromagnetic compatibility (EMC) - Part 4-7: Testing
and measurement techniques - General guide on harmonics
and interharmonics measurements and instrumentation, for
power supply systems and equipment connected thereto, IEC
61000-4-7
[4] Electromagnetic compatibility (EMC) - Part 4-15: Testing
and measurement techniques - Flickermeter - Functional
and design specifications, IEC 61000-4-15
[5] ***Electrical safety in low voltage distribution systems up to
1 000 V a.c. and 1 500 V d.c. – Equipment for testing.
Measuring or monitoring of protective measures – Part 12:
Performance measuring and monitoring devices (PMD),
IEC 61557-12
[6] *** Measuring and Test Equipment Specifications, NASA
Measurement Quality Assurance Handbook - Annex 2,
NASA-HDBK-8739.19-2, Approved: 2010-07-13
[7] *** Measurement Uncertainty Analysis, Principles and
Methods, NASA Measurement Quality Assurance Handbook
- Annex 3, NASA-HDBK-8739.19-3, Approved: 2010-07-13
[8] ***Estimation and Evaluation of Measurement Decision
Risk, NASA Measurement Quality Assurance Handbook -
Annex 4, NASA-HDBK-8739.19-4, Approved: 2010-07-13
[9] ***Uncertainty of measurement. Part 3: Guide to the
expression of uncertainty in measurement (GUM:1995)
ISO/CEI GUIDE 98-3
[10] ***Certificate of Calibration CT/260/2011, for C300 Calmet
Calibrator, Serial number: 21042
[11] ***Certificate of Calibration 1287 of 09/26/2011, for PQ
Analyzer Fluke 435, Serial number N10140

[12] ***Metrological Certificate nr. TM2196551 of 03/24/2011
for Energy static meter A1RLQ+, SN: 2748989
[13] ***User manual – Energy meter Alpha plus
[14] ***Electricity metering equipment (a.c.). Particlar requirements
Part 22: Static meters for active energy (classes 0,2S
and 0,5S), IEC 62053-22:2003
[15] ***User manual – Calmet C300 Calibrator
[16] Olguin, G., An Optimal Trade-off between Monitoring and
Simulation for Voltage Dip Characterization of
Transmission Systems, IEEE/PES Transmission and
Distribution, Conference & Exhibition: Asia and Pacific
Dalian, China, 2005
[17] Bollen, M.H.J, Gu, Y.H.I, Signal processing of power quality
disturbance, IEEE Press, A John Wiley & Sons, INC.,
PUBLICATION, 2006
[18] N. Golovanov. P. Postolache. C. Toader. "Efficiency and
power quality" AGIR Ed. 2009
[19] Szekely, I., Szabo, W., Gerican, C., Systems of data acquisition
and processing, Transilvania University of Brasov,
1997