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Electric Power Systems Research 74 (2005) 915

Hardware implementation of a fuzzy logicscale power system

Abdiyadh 1ty

ust 200

Abstract

A power d in ththe perform powerbeen used to lidateto the system n studibetter perfor eratinexperimentally implemented using MATLAB/Real-Time Windows Target toolbox software on a laboratory set up to model a simple powersystem of 1 KVA machine connected to an infinite bus through a transmission line. Experimental tests and results revealed the effectivenessof FLPSS, especially in vulnerable operating points. 2004 Published by Elsevier B.V.

Keywords: H

1. Introdu

The appprove the dthe focus o[1,2]. The oof continuobances, sucthe stabilizsurementsover a widcontrol menon-lineartroller appeas it consu

CorresponE-mail ad

adnnour@ksu(A.A. Al-Sula

0378-7796/$doi:10.1016/jardware implementation; Fuzzy logic stabilizer; Laboratory scale power system

ction

lication of power system stabilizers (PSS) to im-ynamic performance of a power system has beenf extensive studies for more than three decadesperating point of a power system drifts as a resultus load changes or unpredictable major distur-h as three-phase faults. It is necessary to adapt

er parameters in real-time based on on-line mea-in order to maintain good dynamic performancee range of operating conditions. The fuzzy logicthod has been found to be a good tool to deal withand ill-defined systems. Recently, the fuzzy con-ared to be one of the most convenient techniquesmes less computational time and it is robust [3].

ding author. Tel.: +966 5052 07454; fax: +966 1452 8148.dresses: sosaimi@stc.com.sa (S.A. Al-Osaimi),.edu.sa (A. Abdennour), asulmann@ksu.edu.saiman).

Also, this controller could easily be constructed utilizing asimple microcomputer paired with A/D and D/A converters[4]. The implementation of fuzzy logic power system stabi-lizers has been introduced in a number of publications [58].Possible input signals to the fuzzy excitation controller arethe sensed generator speed deviation and acceleration. Thesesignals are first described by some linguistic variables us-ing the membership function in fuzzy set notation before thefuzzy controller can process them. A database, which con-tains all the decision rules, expressed in linguistic variablesis set up to form the basis for the fuzzy logic operation per-formed by the fuzzy excitation controller to reach a desiredoutput [3]. The aim of this paper is to design and implementa fuzzy logic stabilizer to improve the stability of a 1 KVAlaboratory scale model of power system. The studied systemconsists of an alternator connected to an infinite bus througha short transmission line. The stabilizing signal is computedusing the standard fuzzy membership functions, which de-pend on the speed and acceleration state of the generator. The

see front matter 2004 Published by Elsevier B.V..epsr.2004.10.001Saud A. Al-Osaimi a, , Adel Abdennour b,a Saudi Telecom Company, P.O. Box 152071, R

b King Saud Universi

Received 7 May 2004; received in revised form 23 Aug

system stabilizer using the fuzzy logic is designed and implementeance of the fuzzy logic power system stabilizer (FLPSS) and PID

optimize the parameters of the fuzzy and PID stabilizers. To vaand the results of both stabilizers are compared. The simulatio

mance comparable to that of the PIDPSS over a wide range of opstabilizer on a laboratory

ullaziz A. Al-Sulaiman b

1785, Saudi Arabia

4; accepted 3 October 2004

is paper. Simulation studies are performed to evaluatesystem stabilizer (PIDPSS). Genetic algorithms have

the design, different types of disturbances are appliedes show that the fuzzy stabilizer provides a relativelyg conditions. The FLPSS and PIDPSS have also been

10 S.A. Al-Osaimi et al. / Electric Power Systems Research 74 (2005) 915

influence of the proposed stabilizer is demonstrated throughsimulation studies for different operating conditions and dis-turbances. The performance of this fuzzy logic stabilizer isalso evaluated experimentally in the laboratory.

2. Design of fuzzy logic stabilizer

The design process of FLPSS is consisted of the followingsteps [9]: Selection of FLPSS input/output variables; Fuzzification; Rule definition; Rule inference and Defuzzification.

The block diagram of the FLPSS is shown in Fig. 1. TheFLPSS has two inputs and one output. The speed deviation() and its derivative () are considered as the inputsof the FLPSS. The speed deviation and its derivative passthrough two suitable gains, Ke and Ke, respectively, beforethey are fed to the FLPSS. The output signal of the stabilizeris also scathen is sentgains is tonormalized

Fuzzificvariables toinputs of Ffunctions slinguistic vMP, LP), wsmall negaand large pfunctions aTable 1.

Table 1Output memb

Output subset

LNMNSNVSSPMPLP

For thisables for ea

as shoe. Theed asle 1:e infet mem

ntecedhen them (bee deft into

ficatiot is th

quatio

=

e (ui) denotes the output membership grade for the ith

2on table

t Acceleration ()LN MN SN VS SP MP LP

deviation ()VS SP MP LP LP LP LPSN VS SP MP MP LP LPMN SN VS SP SP MP LPMN MN SN VS SP MP MPLN MN SN SN VS SP MPLN LN MN MN SN VS SPLN LN LN LN MN SN VSled by passing through the output gain, Ku, andto the power system. The main objective of these

allow the use of speed and acceleration signals inquantities.

ation is the process of transferring the crisp inputcorresponding fuzzy variables. In this paper, the

LPSS are fuzzified according to the membershiphown in Fig. 2. For each input variable, sevenariables are defined as (LN, MN, SN, VS, SP,hich indicate large negative, medium negative,

tive, very small, small positive, medium positive,ositive, respectively. Also, the output membershipre chosen as singleton functions as indicated in

Fig. 1. Block diagram of a system with FLPSS.

ership functions

UPSS (pu)0.10.060.03

00.030.060.1

rulesa ruldefin

RuTh

outputhe a0.8, tof th

Thoutpufuzzioutpuing e

UPSS

wherrule.

TableDecisi

Outpu

SpeedLPMPSPVSSNMNLNFig. 2. Membership functions for , .

FLPSS, with two inputs and seven linguistic vari-ch input, there will be a maximum of 49 decisionwn in Table 2. Every entity in the table representsrule relating two inputs and one output can be

the following logic,If () is LP and () is LN, then (UPSS) is VS.rence mechanism is used to compute the FLPSSbership grades. For example, if the two parts of

ent yielded the fuzzy membership values 0.5 ande fuzzy operator simply selects the minimum valuecause of the AND operator), which equals 0.5.uzzifier converts the fuzzy value of the FLPSSa crisp (numerical) value. The input for the de-n process is the aggregated fuzzy output. The finale stabilizer signal UPSS. In this paper, the follow-n is used for defuzzification[10]:

ni=1ui(ui)ni=1(ui)

(1)

S.A. Al-Osaimi et al. / Electric Power Systems Research 74 (2005) 915 11

Table 3Optimal parameters for fuzzy and PID stabilizers

Type Parameter Value

Fuzzy

PID

3. Tuning

In this ptune the fupurposes. Trithm is toiterative mtuning algogiven by:

Fitnees fun

where

Je =N

k=1|

where N istime and G

The whparameters

(i) Initiali(ii) For eac

(iii) Checkto step

(iv) Producand mu

(v) DetermThe FL

a single ouKe, and onparameters

The parasystem mocondition:

Generated

A 0.25 pplied. The pin Table 3lizers are s

Fig. 5 stem with th0.25 pu ste

3. Fitness function for tuning the parameters of the fuzzy stabilizer.

4. Fitness function for tuning the parameters of the PID stabilizer.

he system without stabilizer is highly oscillatory whilexistence of FLPSS or PIDPSS assists in damping thislation. Also one can say that the fuzzy and PID stabi-under this operating condition have the same response,

h is expected since they are both designed and optimizedis operating condition. The fuzzy and PID parameters

ept unchanged for all other tests performed in this paper.Ke 1.15Ke 0.38Ku 65.58

Kp 2.60Ki 1.00Kd 2.95

of FLPSS parameters

aper, a genetic algorithm program has been used tozzy stabilizers and PID stabilizer for comparisonhe aim of the proposed parameter-tuning algo-change the stabilizers gains in an intelligent an

anner to achieve a desired system response. Therithm attempts to maximize the fitness function

ction = G 11 + Je

(k)|

the number of data acquired during simulation, the normalization gain = 100.ole procedure of applying GAs to determine theof PSSs is summarized as [11,12]:

ze population using random selection method;h individual string, compute its fitness function;whether the stopping criterion is met. If yes, go(v), otherwise, continue to step (iv);e new population using reproduction, crossovertation, then go back to step (ii) andine the most fit parameters.

PSS developed in this paper has two inputs andtput, which implies two input parameters Ke ande output parameter Ku. The PIDPSS has threeKp, Ki and Kd.meter-tuning algorithm was applied to the power

del for both stabilizers at the following operating

Fig.

Fig.

that tthe eoscillizerswhicfor thare kpower (P) = 0.5pu, power factor = 0.95u step decrease in the mechanical power is ap-arameters of both stabilizers after tuning are listed

and the fitness functions for fuzzy and PID stabi-hown in Figs. 3 and 4, respectively.hows the speed deviation response for the sys-e fuzzy, with PID, and without a stabilizer, underp decrease in torque at P= 0.5 pu. It can be seen Fig. 5. Response to 0.25 pu step decre