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Electric Power Systems Research 74 (2005) 9–15

Hardware implementation of a fuzzy logic stabilizer on a laboratoryscale power system

Saud A. Al-Osaimia, ∗, Adel Abdennourb, Abdullaziz A. Al-Sulaimanb

a Saudi Telecom Company, P.O. Box 152071, Riyadh 11785, Saudi Arabiab King Saud University

Received 7 May 2004; received in revised form 23 August 2004; accepted 3 October 2004

Abstract

A power system stabilizer using the fuzzy logic is designed and implemented in this paper. Simulation studies are performed to evaluatethe performance of the fuzzy logic power system stabilizer (FLPSS) and PID power system stabilizer (PIDPSS). Genetic algorithms havebeen used to optimize the parameters of the fuzzy and PID stabilizers. To validate the design, different types of disturbances are appliedt a relativelyb e also beene le powers ffectivenesso©

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o the system and the results of both stabilizers are compared. The simulation studies show that the fuzzy stabilizer providesetter performance comparable to that of the PIDPSS over a wide range of operating conditions. The FLPSS and PIDPSS havxperimentally implemented using MATLAB/Real-Time Windows Target toolbox software on a laboratory set up to model a simpystem of 1 KVA machine connected to an infinite bus through a transmission line. Experimental tests and results revealed the ef FLPSS, especially in vulnerable operating points.2004 Published by Elsevier B.V.

eywords:Hardware implementation; Fuzzy logic stabilizer; Laboratory scale power system

. Introduction

The application of power system stabilizers (PSS) to im-rove the dynamic performance of a power system has been

he focus of extensive studies for more than three decades1,2]. The operating point of a power system drifts as a resultf continuous load changes or unpredictable major distur-ances, such as three-phase faults. It is necessary to adapt

he stabilizer parameters in real-time based on on-line mea-urements in order to maintain good dynamic performancever a wide range of operating conditions. The fuzzy logicontrol method has been found to be a good tool to deal withon-linear and ill-defined systems. Recently, the fuzzy con-

roller appeared to be one of the most convenient techniquess it consumes less computational time and it is robust[3].

∗ Corresponding author. Tel.: +966 5052 07454; fax: +966 1452 8148.E-mail addresses:[email protected] (S.A. Al-Osaimi),

[email protected] (A. Abdennour), [email protected]. Al-Sulaiman).

Also, this controller could easily be constructed utilizinsimple microcomputer paired with A/D and D/A convert[4]. The implementation of fuzzy logic power system stlizers has been introduced in a number of publications[5–8].Possible input signals to the fuzzy excitation controllerthe sensed generator speed deviation and acceleration.signals are first described by some linguistic variablesing the membership function in fuzzy set notation beforefuzzy controller can process them. A database, whichtains all the decision rules, expressed in linguistic variais set up to form the basis for the fuzzy logic operationformed by the fuzzy excitation controller to reach a desoutput[3]. The aim of this paper is to design and implema fuzzy logic stabilizer to improve the stability of a 1 KVAlaboratory scale model of power system. The studied syconsists of an alternator connected to an infinite bus thra short transmission line. The stabilizing signal is compusing the standard fuzzy membership functions, whichpend on the speed and acceleration state of the generato

378-7796/$ – see front matter © 2004 Published by Elsevier B.V.oi:10.1016/j.epsr.2004.10.001

10 S.A. Al-Osaimi et al. / Electric Power Systems Research 74 (2005) 9–15

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 inFig. 1. TheFLPSS has two inputs and one output. The speed deviation(�ω) and its derivative (�ω) are considered as the inputsof the FLPSS. The speed deviation and its derivative passthrough two suitable gains,Ke andKe, respectively, beforethey are fed to the FLPSS. The output signal of the stabilizeris also scaled by passing through the output gain,Ku, andthen is sent to the power system. The main objective of thesegains is to allow the use of speed and acceleration signals inn

putv r, thei shipf enl SP,M tive,s tive,a rshipf ed inT

TO

O

LMSVSML

Fig. 2. Membership functions for�ω, �ω.

For this FLPSS, with two inputs and seven linguistic vari-ables for each input, there will be a maximum of 49 decisionrules as shown inTable 2. Every entity in the table representsa rule. The rule relating two inputs and one output can bedefined as the following logic,

Rule 1: If (∆ω) is LP and (∆ω) is LN, then (UPSS) is VS.The inference mechanism is used to compute the FLPSS

output membership grades. For example, if the two parts ofthe antecedent yielded the fuzzy membership values 0.5 and0.8, then the fuzzy operator simply selects the minimum valueof them (because of the AND operator), which equals 0.5.

The defuzzifier converts the fuzzy value of the FLPSSoutput into a crisp (numerical) value. The input for the de-fuzzification process is the aggregated fuzzy output. The finaloutput is the stabilizer signalUPSS. In this paper, the follow-ing equation is used for defuzzification[10]:

UPSS=∑n

i=1uiµ(ui)∑ni=1µ(ui)

(1)

whereµ(ui) denotes the output membership grade for theithrule.

Table 2D

O

SLMSVSML

ormalized quantities.Fuzzification is the process of transferring the crisp in

ariables to corresponding fuzzy variables. In this papenputs of FLPSS are fuzzified according to the memberunctions shown inFig. 2. For each input variable, sevinguistic variables are defined as (LN, MN, SN, VS,

P, LP), which indicate large negative, medium negamall negative, very small, small positive, medium posind large positive, respectively. Also, the output membe

unctions are chosen as singleton functions as indicatable 1.

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

able 1utput membership functions

utput subset UPSS(pu)

N −0.1N −0.06N −0.03S 0P 0.03P 0.06P 0.1

ecision table

utput Acceleration (�ω)

LN MN SN VS SP MP LP

peed deviation (�ω)P VS SP MP LP LP LP LPP SN VS SP MP MP LP LPP MN SN VS SP SP MP LPS MN MN SN VS SP MP MPN LN MN SN SN VS SP MPN LN LN MN MN SN VS SPN LN LN LN LN MN SN VS

S.A. Al-Osaimi et al. / Electric Power Systems Research 74 (2005) 9–15 11

Table 3Optimal parameters for fuzzy and PID stabilizers

Type Parameter Value

Fuzzy Ke 1.15Ke 0.38Ku 65.58

PID Kp 2.60Ki 1.00Kd 2.95

3. Tuning of FLPSS parameters

In this paper, a genetic algorithm program has been used totune the fuzzy stabilizers and PID stabilizer for comparisonpurposes. The aim of the proposed parameter-tuning algo-rithm is to change the stabilizers gains in an intelligent aniterative manner to achieve a desired system response. Thetuning algorithm attempts to maximize the fitness functiongiven by:

Fitnees function= G × 1

1 + Je

where

Je =N∑

k=1

|�ω(k)|

whereN is the number of data acquired during simulationtime andG, the normalization gain = 100.

The whole procedure of applying GA’s to determine theparameters of PSS’s is summarized as[11,12]:

(i) Initialize population using random selection method;(ii) For each individual string, compute its fitness function;

(iii) Check whether the stopping criterion is met. If yes, goto step (v), otherwise, continue to step (iv);

( over

(

andaK eep

wers tingc

G

s ap-p istedi bi-l

sys-t der0 n

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

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

that the system without stabilizer is highly oscillatory whilethe existence of FLPSS or PIDPSS assists in damping thisoscillation. Also one can say that the fuzzy and PID stabi-lizers under this operating condition have the same response,which is expected since they are both designed and optimizedfor this operating condition. The fuzzy and PID parametersare kept unchanged for all other tests performed in this paper.

Fig. 5. Response to 0.25 pu step decrease in torque atP= 0.5 pu.

iv) Produce new population using reproduction, crossand mutation, then go back to step (ii) and

v) Determine the most fit parameters.

The FLPSS developed in this paper has two inputssingle output, which implies two input parametersKe ande, and one output parameterKu. The PIDPSS has thrarametersKp, Ki andKd.

The parameter-tuning algorithm was applied to the poystem model for both stabilizers at the following operaondition:

enerated power (P) = 0.5pu, power factor= 0.95

A 0.25 pu step decrease in the mechanical power ilied. The parameters of both stabilizers after tuning are l

n Table 3and the fitness functions for fuzzy and PID staizers are shown inFigs. 3 and 4, respectively.

Fig. 5 shows the speed deviation response for theem with the fuzzy, with PID, and without a stabilizer, un.25 pu step decrease in torque atP= 0.5 pu. It can be see


Fig. 6. Power angle response of FLPSS and PIDPSS for a 0.05 pu step changein field voltage,P= 0.25 pu.

Also, it is noted here that the field voltage is limited to−2 to+2 pu in all tests.

The behavior of the fuzzy and PID stabilizers under var-ious types of disturbances and with the system operating atdifferent operating conditions is studied in the following sec-tions.

4. Simulation tests

The system dynamic behavior is investigated through var-ious types of disturbances applied to the system under differ-ent operating conditions. The objectives of this investigationare to see the impact of the fuzzy and the PID stabilizers onthe system dynamics performance as well as to compare thetwo stabilizers responses.

4.1. Reference voltage disturbance test

The performances of the PIDPSS and the FLPSS are eval-uated for the generator operating atP= 0.25 pu and 0.9 powerfactor lagging. A 0.05 pu decrease in reference voltage wasapplied at time 1 s and the system returns to the original valueat time 3 s. The system response for both the fuzzy and PIDstabilizers are shown inFig. 6. This figure shows that thep enti-c

4

valu-a erf s ap-p e att PIDs esb

Fig. 7. Power angle response of FLPSS and PIDPSS for 0.25 pu step changein torque,P= 0.9 pu.

Fig. 8. Power angle response of FLPSS and PIDPSS for a 0.15 pu loadconnected at generator terminals,P= 0.25 pu.

4.3. Sudden connection of resistive load

The performances of the PIDPSS and the FLPSS is evalu-ated for the generator operating atP= 0.25 pu and 0.9 powerfactor lagging. A 0.15 pu resistive load is suddenly connectedat the generator terminals. The generator power angle re-sponse for both stabilizers is shown inFig. 8. It is visible thatthe FLPSS slightly better the PIDPSS.

From the above results, One can conclude that the FLPSSand PID stabilizers under the light disturbance are similar.However, under the large disturbance, the fuzzy stabilizerappears to provide better performance.

5. Experimental studies

Experimental studies have been performed on the machinesystem available in the laboratory to test the fuzzy logic-based PSS. Using a personal computer (PC), the proposed

ower angle responses for both stabilizers are almost idal.

.2. Step change in input power

The performances of the PIDPSS and the FLPSS are eted for the generator operating atP= 0.25 pu and 0.95 pow

actor lagging. A 0.25 pu decrease in reference torque walied at time 1 s and the system returns to original valu

ime 3 s. The system response for both the fuzzy andtabilizers is shown inFig. 7. In this case, the FLPSS givetter performance.


Fig. 9. Schematic diagram of the physical model.

FLPSS has been implemented on a physical model of a powersystem. This system consists of a 1 KVA, 380 V, three-phasealternator driven by a 1 KVA separately excited dc machineand is connected to an infinite bus (city of Riyadh utility bus)

through a transmission line. An overall schematic diagram ofthis physical model is shown inFig. 9.

5.1. Speed measurement

In the proposed FLPSS, generator speed deviation andacceleration are used as the inputs. The speed signal can beobtained from a tacho generator, which is coupled with themachine shaft and generates an analog voltage signal (1 Vper 1000 rpm). For the speed deviation and its accelerationsignals, there is no direct transducer available on the physicalmodel of the power system. However, the speed signal is usedin the software program to generate a signal proportional tospeed deviation and its acceleration.

5.2. Data acquisition system (DAS)

The PC communicates with the outside environmentthrough a data acquisition card. This card is equippedwith sixteen inputs with 12 bit dynamic range and ana-logue interface channels[13]. These channels have a

Fig. 10. Real time implementation of the fuzzy logic stabi

Fig. 11. Real time implementation of the PID stabilize

lizer using MATLAB/Real Time Windows Target toolbox.

r using MATLAB/Real Time Windows Target toolbox.


Fig. 12. Response to 0.45 pu step decrease in torque with FLPPSS and with-out PSS,P= 0.9 pu.

Fig. 13. Response to 0.45 pu step decrease in torque with FLPPSS andPIDPSS,P= 0.9 pu.

Fig. 14. Response to 0.3 pu load connected at generator terminals withFLPPSS and without PSS,P= 0.3 pu.

Fig. 15. Response to 0.3 pu load connected at generator terminals withFLPPSS and PIDPSS,P= 0.3 pu.

selectable sampling rate with a maximum of 330 kHz.This card has, also, two output-range analogue interfacechannels.

5.3. FLPSS Implementation Using MATLAB

The MATLAB/Real Time Windows Target toolbox[14],is used to implement the FLPSS, as shown inFig. 10. Thisprogram works as follows:

The analogue to digital (A/D) input channel of DAS re-ceives the speed signal and samples it at a 1 ms rate. The signalpasses through a low-pass filter to eliminate noise. Then, theacceleration is calculated by using a derivative block. The twosignals (�ω, �ω) are fed to the fuzzy logic controller. Then,the control signal is determined and passed through a satura-tion limiter. Finally, the digital to analog (D/A) output chan-nel of DAS sends the control signal to the exciter-summingjunction.

For comparison, a digital PID power system stabilizer wasbuilt using the same program. A block diagram for PIDPSSimplementation is shown inFig. 11.

5.4. Experiments and test results

The performance of the FLPSS is tested on a physicalm paredt

5ated

f us onsesf n inF

PSSt ob-s t can

odel under various disturbances. The results are como those of a PID PSS.

.4.1. Step change in input powerThe dynamic performance of the system is evalu

or loading conditionsP= 0.9 and 0.96 lag. Pf. A 0.45 ptep decrease in torque is applied. The system respor the FLPSS, PIDPSS and without stabilizer are showigs. 12 and 13, receptively.

From the test results, the impact of the FLPSS and PIDo damp the oscillation of rotor speed deviation can beerved in comparison to the system without PSS. Also, i


be seen fromFig. 12 that the FLPSS offers a slight perfor-mance improve over PIDPSS.

5.4.2. Sudden connection of resistive loadThe dynamic performance of the system is evaluated for

loading conditionsP= 0.3 and 0.88 lag. Pf. A 0.3 pu resistiveload is suddenly connected at the generator terminals. Thegenerator speed deviations for both stabilizers are shown inFigs. 14 and 15, respectively.

From these figures, the FLPSS still yields satisfactory re-sults. The fuzzy stabilizer damps the oscillations in about onecycle. It can be seen from the results that the oscillations ofthe system are damped rapidly.

6. Conclusions

The performance of the fuzzy logic stabilizer is studiedon a physical model composed of a 1 KVA machine con-nected to an infinite bus. In order to realistically evaluatethe performance of the proposed fuzzy PSS, the parametersof PIDPSS are optimized using genetic algorithms and usedas a reference. Extensive simulation studies on this powersystem model have been performed under different operat-ing conditions. The results showed that the performance ofFLPSS is slightly better than the PIDPSS over a wide range ofo ex-p odelw singM ntt per-i sultsr e ini temm

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