CH.S.K.B.Pradeepkumar, V.S.R.Pavan Kumar.Neeli / International Journal of Engineering           Research and Applications ...
in some literature [17, 18].                         3.1   Full order model         However, there is no systematic proced...
4     FUZZY LOGIC POWER SYSTEM STABILIZER                     The proposed controller also uses 7 linguistic              ...
(i) Single machine connected to infinite bus:-                                                                            ...
REFERENCES                                                                                       [1]   Y.Y. Hsu, S.W. Shyu...
June 1996.  [13]  M. Chetty, “A discrete mode        fuzzy power system stabilizer,” Monash        University, Aust...
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  1. 1. CH.S.K.B.Pradeepkumar, V.S.R.Pavan Kumar.Neeli / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue4, July-August 2012, pp.1814-1819 Enhancement Of Power System Stability Using Fuzzy Logic Based Power System Stabilizer CH.S.K.B.Pradeepkumar V.S.R.Pavan Kumar.Neeli Asst.Professor Asst.Professor Department of Electrical & Electronics Department of Electrical & Electronics Engineering Engineering Eluru College of Engineering & Technology Sir C R Reddy College of Engineering Eluru, India Eluru, IndiaAbstract Electromechanical oscillations in a power Disastrous consequences to the interconnectedsystem often exhibit poor damping when the Systems stability, leading to partial or totalpower transfer over a corridor is high relative to collapses (blackouts) [6].the transmission strength. Traditional approaches The most common control action toto aid the damping of power system oscillations enhance damping of the power system oscillationsinclude the use of Power System Stabilizers (PSS). is the use of Power System Stabilizers (PSSs). ThePower system stabilizers are used to generate function of this device is to extend stability limitssupplementary control signals for the excitation by modulating generator excitation to providesystem in order to damp out the low frequency damping to the electromechanical oscillations [7]-power system oscillations. This paper describes [9]. They provide good damping; thereby contributethe design procedure for a fuzzy logic based PSS in stability enhancement of the power systems.(FLPSS). Speed Deviation of a synchronous Designing PSS is an important issuemachine and its derivative are chosen as the input from the view point of power systemsignals to the FLPSS. The inference mechanism of stability. Conventional PSSs (referred to asthe fuzzy logic controller is represented by 49 if- CPSSs) use transfer functions designed forthen rules. The proposed technique has the linear models representing the generators at afeatures of a simple structure, adaptivity and fast certain operating point [10, 11]. However, asresponse and is evaluated on a Single machine and they work around a particular operating point ofMulti machine Power system under different the system for which these transfer functions areoperating conditions to demonstrate its obtained, they are not able to provide satisfactoryeffectiveness and robustness. results over wider ranges of operating conditions. In other words, according to the fact that the gains ofKeywords— Power system oscillations, Power the mentioned controller are determined only forsystem stabilizer(PSS), Fuzzy logic based PSS a particular operating condition, they may not yet(FLPSS), Conventional PSS (CPSS). be valid for a wider range around or for other new conditions [12].I. INTRODUCTION This problem is overcome by using Fuzzy The occurrence of low frequency logic based technique for designing of PSSs. Fuzzyelectromechanical oscillations as synchronizing logic systems allow us to design a controllerpower flow oscillations on transmission lines, is using linguistic rules without knowing the exacta direct consequence of dynamical interactions mathematical model of the plant [13, 14]. Thebetween synchronous generators when the system is application of fuzzy logic based PSSs (FLPSSs) hassubjected to perturbations [1]-[4]. This phenomenon been motivated because of some reasons such asoccurs due to dynamical interactions between improved robustness over that obtained usinggroups of generators (a group oscillates against conventional linear control algorithm, simplifiedanother group), or between one generator (or control design for difficult-to-be modeled systemsgroup of generators) and the rest of the and simplified implementation [9, 15]. Fuzzy logicsystem. The first case characterizes the inter-area controllers (FLCs) are very useful in the case where amodes and the second one the local modes of good mathematical model for the plant is notoscillations and they normally have frequencies in available; however, experienced human operatorsthe range of 0.1 to 0.7 Hz and 0.7 to 2.0 Hz, are available for providing qualitative rules torespectively [5]. These modes are worth paying control the system. In some papers to improveattention because they have low natural damping, the performance of FLPSSs, a hybrid FLPSS iswhich can be either very reduced or negative, presented. In [16], a FLC is used with two CPSSs,mainly due to the voltage regulator action and high also Hybrid PSSs using fuzzy logic and/or neuralloading of the power system. This may have networks or Genetic Algorithms have been reported 1814 | P a g e
  2. 2. in some literature [17, 18]. 3.1 Full order model However, there is no systematic procedure The state space form of the synchronousfor designing FLCs. The most common approach is generator model has two main sets of variables whichto define Membership Functions (MFs) and IF- are flux linkages and currents. But these two sets areTHEN rules subjectively by studying an operating mutually dependent so, one of them can be eliminatedsystem or an existing controller. So, an adaptive and express in terms of the based approach was presented in [19] to 3.2 Single machine connected to infinite bus linear modelchoose the parameters of fuzzy system using atraining process. In this technique, an adaptive The synchronous generator experience annetwork was used to find the best parameter of fuzzy oscillatory period which can be classified into asystem. transient period and a steady state or dynamic The proposed method is illustrated on a period .The transient period is the first cycles afterSingle machine and 3-machine 9-bus power system. the disturbance. The consideration on dynamic areaMATLAB/SIMULINK and fuzzy logic toolbox reduces the system model to the third order model.have been used for system simulation. The results Since the interest of this paper is to look afterdemonstrate that the proposed FLPSS provides a small change in the system, the linearized thirdgood damping over a wide range of operating order model is sufficient for the analysis. Theconditions and improves the stability margin of the simplified third order model of synchronoussystem as well. generator connected to infinite bus through a transmission line having resistance Re and reactanceII. EXCITATION SYSTEM MODEL Xe has the following assumption over the full order Excitation system is one of prime model:importance for the proper operation of synchronous 1. Stator winding resistance is neglected.generators. The excitation system can be as simple 2. Balancing conditions are assumed and saturationas a fixed dc power supply connected to the rotor’s effects are neglected.winding of the synchronous generators. The primary 3. Damper winding effect is neglected.function of a synchronous generator excitationsystem is to regulate the voltage at the generatoroutput. On other words, using the excitation systemin any synchronous machine is to control the field Fig. 3.1 Single machine Infinite Bus systemcurrent injected to the rotor. The point ofcontrolling the field current is to regulate the 3.3 Multi machine power system modelterminal voltage of the machine and maintaining the The single line diagram of a 3-machine 9-bus powerterminal voltage constant and hence keeping the system model shown in Fig.3.2 is used to examinesynchronization of the generator. inter-area oscillation control problem. In Fig. 3.2, the generator G1 is considered as reference bus. This system is created especially for the analysis and study of the inter-area oscillation problem [5]. The base MVA is 100 and the system frequency is 50 Hz. This system exhibits inter-area mode of electromechanical oscillations whose frequency varies from 0.35 to 0.75 Hz depending on the operating conditions. Two sets of Conventional PSSs are used; one for the generator (G2) and another one for the generator (G3). Fig. 2.1 Block Diagram ofExcitation systemIII. SYSTEM MODELLING Modeling of the system is an important part of the design. This chapter presents the modeling of the system parts which are; Synchronous machine, Automatic Voltage Regulator and the Power system Fig. 3.2 Single Line diagram of 3-machine 9-bus stabilizer. system 1815 | P a g e
  3. 3. 4 FUZZY LOGIC POWER SYSTEM STABILIZER The proposed controller also uses 7 linguistic The fuzzy logic control algorithm reflects variables such as: Positive Big (PB), Positive the mechanism of control implemented by people, Medium (PM), Positive Small (PS), Zero (ZE), without using any formalized knowledge about the Negative Small (NS), Negative Medium (NM) and controlled object in the form of mathematical models, Negative Big (NB). The membership functions are and without an analytical description of the control chosen to be Triangular. The defuzzification of the algorithm. Here control strategy depends upon a set variables into crisp outputs is tested by using the of rules, which describes the behavior of the center of gravity (COG) method. controller[8]. It generally comprises four principle The two inputs: speed deviation and components: fuzzification interface, knowledge base, acceleration, result in 49 rules for each machine. decision making logic and defuzzification interface. Decision table in 2 shows the result of 49 rules, In fuzzification, the value of input variables are where a positive control signal is for the deceleration measured i.e. it converts the input data into suitable control and a negative signal is for acceleration linguistic values. control. The knowledge base consists of a database and linguistic control rule base. The database provides the necessary definitions, which are used to define the linguistic control rules and fuzzy data manipulation in a fuzzy logic controller[13].The rule base characterizes the control policy of domain experts by means of a set of linguistic control rules. The decision making logic has the capability of Fig. 4.2 Membership functions of Input/ Output stimulating human decision making based on fuzzy Speed Acceleration concepts. deviation NB NM NS ZE PS PM PB The defuzzification performs scale mapping, NB NB NB NB NB NM NM NS which converts the range of values of output NM NB NM NM NM NS NS ZE variables into corresponding universe of discourse. If NS NM NM NS NS ZE ZE PS the output from the defuzzifier is a control action for ZE NM NS NS ZE PS PS PM a process, then the system is a non-fuzzy logic PS NS ZE ZE PS PS PM PM decision system. PM ZE PS PS PM PM PM PB PB PS PM PM PB PB PB PB Table 4.1 Decision Table of 49-rules SIMULATION RESULTS 5.1 Performance Analysis of Proposed Fuzzy based PSS: The system is simulated using MATLAB/Simulink toolbox. The models of the synchronous machine, PSS and the excitation system are linked together to form the overall system representation. A number of studies Fig. 4.1 Design Procedure of FLC involving variety of tests at different system and operating conditions have been conducted to The initial step in designing the FLPSS is evaluate the efficacy of the proposed stabilizer. the determination of the state variables which All results are compared with the represent the performance of the system. The input performance of a conventional PSS. An illustrative signals to the FLPSS are to be chosen from these set of results are presented in the following section. variables. The input values are normalized and For now onwards, the conventional PSS has been converted into fuzzy variables. Rules are executed referred to as CPSS and proposed Fuzzy based PSS as to produce a consequent fuzz y region for ea ch FLPSS. va r i a bl e. The expected value for each variable is found by defuzzifying the fuzzy regions. The 5.2 Results speed deviation ( ∆ω ) of the synchronous The following set of results are designed for different operating conditions using Fuzzy machine and its derivative ( ∆ ώ ) are chosen as Logic Based Procedure and are compared with the inputs to the FLPSS and the output is the stabilizing conventional PSS and without PSS. signal U PSS . 1816 | P a g e
  4. 4. (i) Single machine connected to infinite bus:- 70 Without PSS 0.025 FLPSS cpss 60 CPSS 0.02 flpss without pss 50 0.015 40 Delta (Degrees) 0.01 Speed Deviations 30 0.005 0 20 -0.005 10 -0.01 0 -0.015 -10 0 0.5 1 1.5 2 2.5 -0.02 Time (sec) 0 1 2 3 4 5 6 7 Time (sec) Fig 5.4 Rotor Angular Positions of Generator -3Fig 5.1 Speed changes for ST=0.9+j0.3, XE=0.65 1.5 Without PSS 2 CPSS cpss FLPSS flpss without pss 1 1.5 0.5 1 Pa, P.U Rotor Angle 0 0.5 0 -0.5 -0.5 -1 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8 9 10 Time (sec) Time (Sec) Fig 5.2 Rotor Angle Deviations for ST=0.9+j0.3, Fig 5.5 Generator-1 Accelerating PowerXE=0.65 (ii) Multi Machine System:- 1 Without PSSThe following set of results is carried out for 3- FLPSSmachine and 9-bus system at a particular fault 0.8 CPSSclearing time of 0.5 sec 0.6 100 Witout PSS FLPSS 0.4 Pa, P.U CPSS 80 0.2 60 Delta (degrees) 0 40 -0.2 20 -0.4 0 1 2 3 4 5 6 7 8 9 10 0 Time (Sec) -20 Fig 5.6 Generator -2 Accelerating Power 0 1 2 3 4 5 6 7 8 9 10 Time (sec) Fig 5.3 Rotor Angular Position of Generator-2 1817 | P a g e
  5. 5. REFERENCES [1] Y.Y. Hsu, S.W. Shyue, C.C. Su, “Low 0.8 CPSS frequency oscillation in longitudinal FLPSS power systems: Experience with dynamic 0.6 Without PSS stability of Taiwan’s power system,” IEEE 0.4 Transactions on Power Systems, Vol. 2, No. 1, pp. 92-100, Feb.1987. 0.2 [2] D.N. Koterev, C.W. Taylor, W.A. Mittelstadt, “Model validation for the Pa, P.U 0 August 10, 1996 WSCC system outage,” IEEE Transactions on Power Systems, Vol. -0.2 14, No. 3, pp. 967-979, Aug.1999. [3] G. Rogers, Power System Oscillations, -0.4 Kluwer, Norwell, MA, 2000. [4] M. Klein, G.J. Rogers, P. Kundur, “A -0.6 fundamental study of inter-area oscillations -0.8 in power systems,” IEEE Transactions on 0 1 2 3 4 5 6 7 8 9 10 Time (Sec) Power Systems, Vol. 6, No. 3, pp. 914-921, Aug.1991. Fig 5.7 Generator-3 Accelerating Power [5] P. Kundur, Power System Stability and control 5.3 Discussions: [6] S.M. Deckmann, V.F. da Costo, “A The Comaprisions from the figures 5.1 to 6.0 the power sensitivity model for “Settling time” is more improved for the Fuzzy based electromechanical oscillation studies,” IEEE method when compared with Conventional based Transactions on Power Systems, Vol. 9, No. technique for all operating conditions both in case of 2, pp. 965-971, 1994. Single Machine and Multi Machine Power systems. [7] M.R. Meshkatoddini, “Comparison of The Overshoot ranges are also improved for the UPFC-based stabilizer and PSS Fuzzy based techniques compared to Conventional performances on damping of power system technique in all operating conditions. oscillations,” American Journal of Applied The FLPSS, though rather basic in its control proves Sciences, Vol. 6, No. 3, pp. 401-406, 2009. that it is indeed a good controller due to its [8] Sidhartha Panda, “Power system with PSS simplicity. and FACTS controller: Modelling, Simulation result shows that for different operating simulation and simultaneous tuning conditions, the fuzzy logic power system stabilizer employing genetic algorithm,” (FLPSS) has increased the damping of the system International Journal of Electrical, causing back to it steady state in much less time than Computer, and Systems Engineering, Vol. 1, the conventional power system stabilizer (CPSS). No. 1, pp. 9-18, 2007. [9] S. Panda, “Simultaneous tuning of static var compensator and power5 CONCLUSIONS system stabilizer employing real coded The main Contribution of this paper is to design genetic algorithm,” International Journal and implement an optimal PSS based on two of Electrical Power and Energy Systems different and advanced Fuzzy logic based Engineering, Vol. 1, No. 4, pp. 240-247, techniques and to compare the obtained final 2008. response. [10] N. Hossein Zadeh, “Performance of a The Dynamic behavior and stability enhancement self-tuned fuzzy-logic power system aspects are also studied in this paper. stabilizer in a multimachine system,” The proposed PSS design using Fuzzy Logic method School of Engineering and Science, Monash enhances system response and provides good University, Malaysia. damping to the system Oscillations compared to [11] A.S. Venugopal, “An adaptive neuro Conventional technique. fuzzy power system stabilizer for damping Such a nonlinear fuzzy based PSS will yield better inter-area oscillations in power systems,” and fast damping under small and large disturbances Proceeding of 36th Southeastern even with changes in system operating conditions. Symposium on System Theory, pp. 41-44, Better and fast damping means that generators can September 2004. operate more close to their maximum generation [12] P. Hoang, “Design and analysis of capacity. This ensures that generators remain stable an adaptive fuzzy power system under severe faults such as three phase short circuits. stabilizer,” IEEE Transactions on Energy Conversion, Vol. 11, No. 2, pp. 455-461, 1818 | P a g e
  6. 6. June 1996. [13] M. Chetty, “A discrete mode fuzzy power system stabilizer,” Monash University, Australia. [14] M.A.M. Hassan,” Implementation and laboratory test results for a fuzzy logic based self- tuned power system stabilizer,” IEEE Transactions on Energy Conversion, Vol. 8, No. 2, pp. 221-228, June 1993. [15] J. Lu, “A fuzzy logic-based adaptive power system stabilizer for multi-machine systems,” IEEE Power Eng Soc., 2000. [16] M. Hashem, “A neuro-fuzzy power system stabilizer with self-organizing map for multi- machine systems,” Proceeding of IEEE Power Engineering Society Transmission and Distribution Conference, Vol. 2, pp. 1219-1224, 2002. [17] D. Menniti, “Damping oscillation improvement by fuzzy power system stabilizers tuned by genetic algorithm,” 14th PSCC, Sevilla, 2002. [18] J. Shing Roger Jang, “ANFIS: adaptive- network based fuzzy inference system,” IEEE Transactions on Systems, Man and Cybernetics., Vol. 23, No. 3, pp. 665-685, May/June 1993. [19] Fuzzy Logic Toolbox [20] P.M. Anderson and A.A. Fouad, Power System Control and Stability, Iowa State University Press, Ames, IA, 1977. [21] Y.N. Yu, Electric Power System Dynamics, Academic Press, 1983.Appendix:-(i) Single machine system:Synchronous Machine constants:-Xd=1.81 p.u, Xd1=0.3 p.u, Xq=1.76 p.u, RE=0.003 p.uTdo1=8.0 sec, H=3.5 sec, KD=0, f=50Hz Excitation system constants:-KA=200 TA=0.05 TR=0.02 Conventional PSS data:-Kstab=17.5, Tw=5sec, T1=0.154sec, T2=0.033sec(ii) Multi machine system:Generators Data:-Generator G2:--Xd=0.8958 p.u, Xd1=0.1198 p.u, Xq=0.8645 p.u,Tdo1=6sec, H=6.4 sec, KD=0, f=50Hz, XE=0.4RE=0.003p.u KA=50 TA=0.05 TR=0.02Generator G3:--Xd=1.3125 p.u, Xd1=0.1813 p.u, Xq=1.2578 p.u,Tdo1=5.89sec, H=3.01 sec, KD=0, f=50Hz, XE=0.4RE=0.003p.u KA=50 TA=0.05 TR=0.02Conventional PSS data:-Kstab=4, Tw=3sec, T1=0.1537sec, T2=0.1sec 1819 | P a g e