European Journal of Scientific Research - EuroJournals

A Rule-Based Fuzzy Automatic Voltage Regulator for Power System Stability 925
logic concepts, experts’ knowledge can be used directly to design a controller. Fuzzy logic allows one
to express the knowledge with subjective concepts such as very big, too small, moderate and slighty
deviated, which are mapped to numeric ranges [3].
Fuzzy control implementation of AVR has been reported in a number of publications [4,5,6].
Due to its lower computation burden and its ability to accommodate uncertainties in the plant model,
fuzzy logic PI configured AVR (FAVR) appear to be suitable for implementing AVRs. The
implementation of FAVR is through simple microcomputer with A/D and D/A converters [7]. The
performance of this controller is observed to depend on the operating conditions of the system,
although, it is less sensitive than conventional linear PI controller. In this paper, a rule based fuzzy
logic PI configured controller is developed for excitation and generator terminal voltage control. The
vast rule (11×11) developed for the FAVR made it well suitable for different operating conditions of
the plant. The controller is applied to a mathematical model of the exciter and synchronous generator.
The responses of the machine subject to a fault in the transmission line is obtained by nonlinear
simulations [7]. System responses for three different operating conditions are conducted using FAVR,
conventional PI, and PID controller. All the simulations are carried out using MATLAB software
package. Result shows reduction in overshoot, settling time, rise time and overall responses.
2. Rule – Based Fuzzy Logic AVR
A fuzzy logic controller (FLC) is a special kind of a state variable controller governed by a family of
rules and a fuzzy inference mechanism. The FLC algorithm can be implemented using heuristic
strategies, defined by linguistically described statements. The fuzzy logic control algorithm reflects the
mechanism of control implemented by people, without using any formalized knowledge about the
controlled plant in the form of mathematical models, and without an analytical description of the
control algorithm. The main FLC are fuzzification, rule base, inference mechanism and defuzzification
[8]. Fuzzification is the process of transferring the crisp input variables to corresponding fuzzy
variables. In this work, the error between the desired voltage Vd and the terminal voltage Vt i.e. Ve and
the integral of the error VI are the two inputs. Ve is fuzzified according to the membership functions
shown in fig.1 and is similar to membership function of VI.
For each input variable eleven labels are defined, namely: NV, NL, NB, NM, NS, ZR, PS, PM,
PB, PL and PV which stands for negative very large, negative large, negative big, negative medium,
negative small, zero, positive small, positive medium, positive big, positive large and positive very
large. This numerically stands for (-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 08 and 1). With the two
inputs for this FLC, an (11×11) decision table is constructed as shown in Table 1. Every entity in the
table represents a rule. The antecedent of each rule conjuncts Ve and VI fuzzy set values. An example
of the ith rule is: if Ve is NS and VI is PL then U is PB. This means that if the voltage error is negative
small and integral of voltage error is positive large then the output of controller should be positive big.
Figure 1: Membership functions for input Ve
The procedure for calculating the crisp output of the FLC for some values of input variables is
based on the following three steps [8];