Power system stabilizers (PSS) are added to excitation system to enhance the damping during low frequency oscillations. This paper presents a study of fuzzy logic power system stabilizer for stability enhancement of a single machine connected to infinite bus (SMIB) power system. Recently fuzzy logic as a novel robust control design method has shown promising results. The emphasis in fuzzy control design center is around uncertainties in the system parameters and operating conditions. Fuzzy logic controller (FLC) has been suggested as a possible solution to overcome this problem, thereby using linguist information and avoiding a complex system mathematical model, while giving good performance under different operating conditions. The proposed approach is found to have stable convergence characteristics and resulted in good

voltage regulation and damping characteristics. The stabilizing signals were computed using the fuzzy

membership functions with variations of angular speed and acceleration as the inputs. The simulations were tested under different operating conditions, with different membership functions using MATLAB /SIMULINK. The simulation results are quite encouraging and satisfactory

Hayfaa, Mohammed Hussein Hasan

Marizan, Sulaiman

Mohammed Rasheed, Jubair Al-Hiealy

Mohd Shahrieel, Mohd Aras

Rosli, Omar

Universiti Teknikal Malaysia Melaka (UTeM) Repository

World Applied Sciences Journal 34 (10): 1370-1379, 2016ISSN 1818-4952© IDOSI Publications, 2016DOI: 10.5829/idosi.wasj.2016.Corresponding Author: Hayfaa Mohammed, Faculty of Electrical Engineering, UniversitiTeknikal Malaysia Melaka,Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.1370Comparative Studies of Fuzzy Logic Base Power System Stabilizers inEnhancing Dynamic Stability of a Generator Connected to Infinite BusHayfaa Mohammed, Marizan Sulaiman, Mohammed Rasheed, Rosli Omar and Shahrieel ArasFaculty of Electrical Engineering, UniversitiTeknikal Malaysia Melaka,Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MalaysiaAbstract: Power system stabilizers (PSS) are added to excitation system to enhance the damping during lowfrequency oscillations. This paper presents a study of fuzzy logic power system stabilizer for stabilityenhancement of a single machine connected to infinite bus (SMIB) power system. Recently fuzzy logic as anovel robust control design method has shown promising results. The emphasis in fuzzy control design centeris around uncertainties in the system parameters and operating conditions. Fuzzy logic controller (FLC) hasbeen suggested as a possible solution to overcome this problem, thereby using linguist information andavoiding a complex system mathematical model, while giving good performance under different operatingconditions. The proposed approach is found to have stable convergence characteristics and resulted in goodvoltage regulation and damping characteristics. The stabilizing signals were computed using the fuzzymembership functions with variations of angular speed and acceleration as the inputs. The simulations weretested under different operating conditions, with different membership functions using MATLAB /SIMULINK.The simulation results are quite encouraging and satisfactory.Key words: Power System Stabilizer  Stability  Controller Design  Fuzzy Logic ControllerINTRODUCTION to provide damping and voltage regulation. On the otherThe power system is a dynamic system. It is techniques like Particle-Swarm Optimization (PSO) haveconstantly being subjected to disturbances, according to led to a new approach in solving complex optimizationwhich generator voltage angle changes. When these problems.PSS is a device which provides additionaldisturbances removed, a new corrective steady state supplementarycontrol loops to the automatic voltageoperating condition is reached. It is important that these regulators system and/or the turbine governing system ofdisturbances do not drive the system to unstable a generating unit [2-4]. The common feature of AVR/PSScondition. The disturbances may be of local mode having controllers is that they are typically based on modelsfrequency range of 0.7 to 2 Hz or of inter area modes established by approximate linearization of the nonlinearhaving frequency range in 0.1 to 0.8 Hz, these swings are equations of a power system at certain operating point.dueto the poor damping characteristics caused by modern But, in case of large disturbances, these linearized controlvoltage regulators with high gain. A high gain regulator techniques are not effective. In an attempt to cover athrough excitation control has an important effect of wider range of operating conditions, expert or rule-basedeliminating synchronizing torque but it affects the controllers have also been proposed. Recently, fuzzy logicdamping torque negatively [1]. A high-gain fast-response as a novel robust control design method has shownAVR improves the voltage regulation and improves the promising results.ability of the power system to maintain synchronism when A fuzzy Logic Controllers (FLCs) have attractedsubjected to large disturbances. A PSS is directly considerable attention as candidates for novelconnected to the AVR of synchronous generators and the computational systems because of the variety of themain aim of PSS-AVR excitation control configuration is advantages     they       offer        over      the   conventionalhand, the advancement in the bio-inspired and evolutionWorld Appl. Sci. J., 34 (10): 1370-1379, 20161371computational systems. The application of a power Therefore, it is very difficult to design a stabilizer thatsystem stabilizer for improving the stability ofpower could present good performance in all operating points ofsystems has received much attention. Fuzzy control electric power systems. In an attempt to cover a wideappears to be the most suitable one, due to its robustness range ofoperating conditions, Fuzzy logic control hasandlower computation burden. Fuzzy logic controller been suggested as a possible solution to overcome this(FLC) is well-known with its potential to obtain superior problem, thereby using linguist information and avoidingspeed control in high performance drives. It has proved a complex system mathematical model, while giving goodby many performance analyses as well comparative performance under different operatingstudies either through simulation or experimental conditions.Limitations of fixed parameter of conventionalverifications [5-8]. A fuzzy controller is designed to PSS has to lead advanced control schemes such as selfemulate human deductivethinking, that is, the process tuning, rulebased PSS and fuzzy logic control. For steadypeople use to infer conclusions (decisionmaking) from state operation, the system with fuzzy logic PSS settleswhat they know. Fuzzy control has been primarilyapplied down much faster than system with conventional PSS. Forto  the  control  of processes through fuzzy  linguistic. three- phase to groundfault operation, the oscillationThe most common fuzzy logic controllers are based on under system with fuzzy logic PSS decays faster thanconstant parameter fuzzy logic (CPFL). The CPFL under system with conventional PSS. And hence thecontroller has already been successfully implemented in design of PSS becomes model dependent. The majorhigh performance vector controlled drive [9, 10]. It is advantage of fuzzy logic controller is its modelenvisaged that it is possible to take full advantages of independency. But the parameters of fuzzy logiccontrollerFLC for UUV application if the computational time of FLC are determined by trial and error method. Thismethod isis minimized. The Single Input Fuzzy Controller (SIFLC) is time-consuming and does not guarantee an optimalproposed. Synchronous generator stability can be controller [17-19].enhanced either by monitoring his excitation and hencethe field current via anexciter-based power system System Modeling: The Mathematical Models needed forstabilizer (EPSS), or bycontrolling the governor through small signal analysis of Synchronous Machines,a so called governor powersystem (GPSS) which offers Excitation System and lead-lag power system stabilizer areadjustment of mechanical torque. The inconvenience of briefly reviewed. The Guidelines for the selection of Powerthis method is its slower simulation times and its System Stabilizer parameters are also presented.important hardware resources request. Other researchershave attempted to implement FLC in the hardware for Synchronous Machine Model: The synchronous machinedifferent applications during the past few  years  [11-13]. is vital for power system operation. The general systemA fuzzy logic-based controller adjusts the system input to configuration of synchronous machine connected toget a desired output by just looking at the output without infinite bus through transmission network can beany requirement of mathematical model for controlled represented as the mathematical models needed for smallsystem that are now being exploited in motorcontrol signal analysis of synchronous machine; excitationapplications. For this reason, fuzzy logic-based controller system and the lead-lag power system stabilizer are brieflysystems differ from classical control systems and it is reviewed. The guidelines for the selection of powerpossible to get desired control actions for complex, system stabilizer parameters are also presented. Theuncertain and non-linear systems by using fuzzy logic Thevenin’s equivalent circuit shown in Fig. 2.1.controller (FLC) without the requirement of theirmathematical models and parameter estimation. All along, Classical  System   Model:    The   generator isfuzzy logic systems (FLSs) are the very important tool to represented as the voltage E' behind  Xd'  as  shown inapproximate unknown nonlinearities in complex dynamic Fig. 2.2. The magnitude of E' is assumed tosystems and have received considerable attention. Fuzzy remainconstant at the pre-disturbance value. Let  be thelogic has been successfully used in numerous fieldssuch angle by which E' leads the infinite bus voltage E . The as control systems engineering, power engineering, changes withrotor oscillation. The line current and totalindustrial automation and optimization. FLC has proven power can be expressed aseffective for complex, non-linear and imprecisely definedprocesses for which standard model based control (1)techniques are impractical or impossible [14-16].B2S S + 02 2S OD K WKH H+ =World Appl. Sci. J., 34 (10): 1370-1379, 20161372Fig. 2.1: Single Machine Infinite Bus System.Fig. 2.2: Classical model of generatorFig. 2.3: Block diagram of single machine infinite bussystem with classical modelS = P + jQ = + (2)The above equation to describe small-signalperformance is represented in schematic Fig. 2.3 [1].From the block diagram we have [1] = (3)(4)Solving the block diagram we get the characteristicsequation:(5)Comparing it with general form, the undamped naturalfrequency  and dampingratio ( ) Are expressed as [1].nFig. 3.1: Principle design of fuzzy logic controller(6)(7)MethodologyPSS Model: Power System Stabilizer (PSS) is a devicewhich provides additional supplementary control loops tothe automatic voltage regulator system and/or the turbinegoverning system of a generating unit. PSS are often usedas an effective and economic means of damping suchoscillations. Adding supplementary control loops tothegenerator AVR is one of the most common ways ofenhancing both small-signal (steady state) stability andlarge-signal (transient) stability. PSS is working in parallelwith the excitation system in order to modify the powerangle to increase the damping of the oscillation. Since thePSS and the excitation system are working in parallel, theperformance of the excitation system is very important toPSS [4, 1].(8)Fuzzy Controller: The fuzzy control systems are rule-based systems in which a set of fuzzy rules represent acontrol decision mechanism to adjust the effects of certainsystem stimuli. With the help of effective rule base, fuzzycontrol systems can replace a skilled human operator. Thefuzzy logic controller provides an algorithm which canconvert the linguistic control strategy based on expertknowledge into an automatic control strategy. The Fig. 3.1illustrates the schematic design of a fuzzy logic controllerwhich consists of a fuzzification interface, a knowledgebase, control system (process), decision making logic anda defuzzification interface[1].World Appl. Sci. J., 34 (10): 1370-1379, 20161373Fig. 3.2: Fuzzy logic based PSSTable 4.1: Machine dataparameters Numerical valueK 0.98311K 1.09232K 0.38643K 1.47464K -0.11035K 0.44776K 0.08ET 0.25EK 0DH 6D 0.95Table 4.2: PSS Parameters designed by PSOT1 T2 T3 T4 Kpss0.3730 0.1096 0.7910 0.0819 7.1144Table 4.31: machine dataParameters Numerical valueK 0.76351K 0.86432K 0.32303K 1.41884K -0.14625K 0.41666K 200ET 0.0132EK 0DH 3.5Table 4.31: machine dataParameters Numerical valueK 0.76351K 0.86432K 0.32303K 1.41884K -0.14625K 0.41666K 200ET 0.0132EK 0DH 3.5The  fuzzy   logic  controller   block   consists offuzzy   logic    Block   and   scaling   factors.  Scalingfactors inputs are two & one for each input and onescaling  factor   for    output    which    determine   theextent   to   which   controlling   effect   is  produced bythe  Fuzzy  Logic  controller  shown in Fig. 3.2.Performance  of  Fuzzy   Logic   controller  is studied forthe scaling factors having the values as Kin =2, Kin =30,1 2K =0.01 [1].outSimulation ResultsSystem 1: The studies were implemented usingMATLAB/SIMULINK    based     on     the    systemshown  Fig    4.1. performance    of    single  machineinfinite    bus      system      hasbeen    studied  (1) withAVR (2)FLC based PSS and (3) With PSS (lead-lag)designed  by   PSO.   The  machinedata is taken fromTable 4.1 [4].The variations of angular speed,angular position,torque and voltage are displayed in Fig 4.2 for thesystems with 20% change of input torque. PSO is used inthis paper to tune PSSparameters as described in theprevious section. Table 4.2 presents the PSS parametersas designed by PSO taken from [4].System 4.2: For the studies were carried out for thesystems shown in Fig 4.3. The performance of singlemachine infinite bus system hasbeen studied (1) withAVR (2)PSS (3)With FLC.The machinedata is taken fromreferences [1-3].System 4. 2: For the studies were carried out for thesystems shown in Fig 4.3. The performance of singlemachine infinite bus system hasbeen studied (1) withAVR (2)PSS (3)With FLC.The machinedata is taken fromreferences [1-3].Results are shown in Fig. 4.4 and Fig. 4.5 with fuzzylogic controller the variation in (a) Angular Speed, (b)Angular Position (c) Torque Variations and (d) VoltageVariations.with K positive = 0.1462 and K negative = -5 50.1462 respectively.Fuzzy  Infereence  System:  Fuzzy  logic  block isprepared using Fuzzy Inference System file in Matlab(R2013a) and the basic structure of this FIS editor file asshown in Fig. 4.6. This is implemented using following FIS(Fuzzy Inference System) properties as shown in Fig 4.64.9 [1].0 102030405060-0.0500.050.10.150.20.250.3TimeAngular Position0 10 20 30 40 50 6000.511.522.533.5TimeAngular Position0 10 20 30 40 50 60-0.8-0.6-0.4-0.200.20.40.60.811.2TimeTorque Variations0 10 20 30 40 50 60-1.2-1-0.8-0.6-0.4-0.200.2TimeVoltage VariationsWorld Appl. Sci. J., 34 (10): 1370-1379, 20161374Fig. 4.1: Simulation models of AVR, FLC andPSO.(a) Angular Speed (b) Angular Position(c) Torque Variations (d) Voltage VariationsFig. 4.2: Simulation result of AVR with FLC based PSS designedPSO with K -0.1103.5=0 10 20 30 40 50 60-0.0500.050.10.150.20.250.3TimeAngular Position0 10 20 30 40 50 6000.511.522.533.5TimeAngular Position0 10 20 30 40 50 60-0.8-0.6-0.4-0.200.20.40.60.811.2TimeTorque Variations0 10 20 30 40 50 60-1.2-1-0.8-0.6-0.4-0.200.2TimeVoltage VariationsWorld Appl. Sci. J., 34 (10): 1370-1379, 20161375Fig. 4.3: Simulation models of AVR, PSS and FLC.(a) Angular Speed (b) Angular Position(c) Torque Variations (d) Voltage VariationsFig. 4.2: Simulation result of AVR with FLC based PSS designedPSO with K -0.1103.5=World Appl. Sci. J., 34 (10): 1370-1379, 20161376Fig. 4.3: Simulation models of AVR, PSS and FLC.Fig. 4.4: Simulation results of AVR, PSS and FLC with k5 positive K = 0.1462.5World Appl. Sci. J., 34 (10): 1370-1379, 20161377Fig. 4.5: Simulation results of AVR, PSS and FLC with k5 negative K -0.1462.5=Fig. 4.6: Fuzzy Inference SystemFig. 4.7: Rule Viewer of Fuzzy ControlWorld Appl. Sci. J., 34 (10): 1370-1379, 20161378Fig. 4.8(a) Membership functions for speed deviation power through the transmission lines and leads to externalFig. 4.8: (b) Membership functions for acceleration oscillation.Fig. 4.8: (c) Membership functions for voltageFig. 4.9: Rule Base for Fuzzy Logic Controller Dynamic  Stability  Enhancement  using ParticleCONCLUSION pp: 0-4.In these comparative studies, the fuzzy logic using T-S Fuzzy Model for Multimachine Powercontroller based power system stabilizer is designed for Systems.Single Machine Infinite Bus (SMIB) Power System. The 6. Ali, Z.M.M., 2012. Improving Power System Stabilityperformance of the power system with fuzzy logic power Using A Fuzzy Logic Control, pp: 182-186.system stabilizer is better one since it is effective for alltest conditions. The control signal, required, in all casesis with less magnitude. The proposed fuzzy logiccontroller based PSS is useful for power stations whichwork under small and large signal disturbances. Smallchange in the load torque disturbance that affects itsefficiency and sometimes leads to unstable system. Thesedisturbances cause oscillations at low frequencies that areundesirable since it affects the amount of transferredstress to the mechanical shaft. To avoid such situation apower system stabilizer is added to the Automatic VoltageRegulator (AVR) to enhance stability in the dynamicrange as well as in the first few cycles after a disturbance.This fuzzy logic controller is very suitable for the real timecontrol of generators because of its simple control rulesand its shorter computation time.The proposed PSSdesignbased on fuzzy logic controller enhances thesystem response and provide good damping to theACKNOWLEDGMENTThis authors are grateful to UniversitiTeknikalMalaysia Melaka (UTeM) for partly supportingthisr e s e a r c h t h r o u g h  t h e  g r a n tFRGS/2/2014/TK03/FKE/01/F00238.REFERENCES1. Kushwaha, M., 2013. Dynamic Stability Enhancementof Power System using Fuzzy Logic Based PowerSystem Stabilizer, 2013 Int. Conf. Power, EnergyControl, pp: 213-219.2. Selvabala, B., 2010. Co-Ordinated Tuning Of AVR-PSS Using Differential Evolution  Algorithm,  IEEE,pp: 439-444.3. Ahmad, A.H., D. Ph and A.A. Abdelqader, 2011.Power System Stabilizer Design Using Real-CodedGenetic Algorithm, 2011 2  Int. Conf. Control.ndInstrum. Autom., 2011.4. M.  and     S.M.A.H.A.S.      Al-Hinai,      2009.Swarm Optimization Power System Stabilizer, IEEE,5. Abbadi, A., 2012. A Nonlinear Voltage ControllerWorld Appl. Sci. J., 34 (10): 1370-1379, 201613797. Hitesh, C.M. and D. Patel, 2011. Fuzzy Logic 14. Yi, Y., W. Zheng, S. Changyin and L. Guo, 2015. DOBApplication to Single Machine Power System Fuzzy Controller Design for Non-Gaussian StochasticStabilizer, pp: 8-10. Distribution Systems Using Two-Step Fuzzy8. Noormiza,  S.,  M.   Isa,   Z.   Ibrahim,  J.M.  Lazi  and Identification, IEEE Trans. Fuzzy Syst., 6706: 1-1.U. 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Single Input Fuzzy Logic Controller for Fuzzy logic based PSS for positive and negativeUnmanned Underwater Vehicle, J. Intell. Robot. Syst., value of K  constant, Comput. Commun. Electr.59(1): 87-100. Technol.  (ICCCET),  2011  Int.  Conf.,  no.  March,12. Mayouf, F., F. Djahli, A. Mayouf and T. Devers, 2013. pp: 430-435.Multi-machine Fuzzy Logic Excitation and Governor 19. Vakula, V.S. and K.R. Sudha, 2012. Design ofStabilizers Design Using Genetic Algorithms. differential evolution algorithm-based robust fuzzy13. Gdaim, S., A. Mtibaa and M.F. Mimouni, 2015. Design logic power system stabiliser using minimum ruleand Experimental Implementation of DTC of an base, IET Gener. Transm. Distrib., 6: 121.Induction Machine Based on Fuzzy Logic Control onFPGA, 23(3): 644–655.5

Comparative Studies Of Fuzzy Logic Base Power System Stabilizers In Enhancing Dynamic Stability Of A Generator Connected To Infinite Bus

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Comparative Studies Of Fuzzy Logic Base Power System Stabilizers In Enhancing Dynamic Stability Of A Generator Connected To Infinite Bus

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