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  1. 1. A.V.Sudhakara Reddy, M. Ramasekhara Reddy, Dr. M. Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1560-1565 Stability Improvement During Damping of Low Frequency Oscillations with Fuzzy Logic ControllerA.V.Sudhakara Reddy1, M. Ramasekhara Reddy 2, Dr. M. Vijaya Kumar3 1 (M. Tech, Department of Electrical & Electronics Engg, JNTUACE, Pulivendula, A.P, India.) 2 (Assistant Professor, Department of Electrical & Electronics Engg, JNTUACE, Pulivendula, A.P, India.) 3 ( Professor, Department of Electrical & Electronics Engg, JNTUACE, Anantapur, A.P, India.)Abstract Low Frequency Oscillations (LFO) transmission system near their limits. Thus, theoccur in power systems because of lack of the occurrence of the LFO has increased. FACTsdamping torque in order to dominance to power Controllers has capability to control networksystem disturbances as an example of change in conditions quickly and this feature of FACTs can bemechanical input power. In the recent past used to improve power system stability. The UPFCPower System Stabilizer (PSS) was used to damp is a FACTS device that can be used to the LFO. TheLFO. FACTs devices, such as Unified Power primarily use of UPFC is to control the power flowFlow Controller (UPFC), can control power flow, in power systems. The UPFC consists of tworeduce sub-synchronous resonance and increase voltage source converters (VSC) each of them hastransient stability. So UPFC may be used to two control parameters namely me, δe ,mb and δbdamp LFO instead of PSS. UPFC damps LFO [3]. The UPFC used for power flow control,through direct control of voltage and power. A enhancement of transient stability, mitigation ofcomprehensive and systematic approach for system oscillations and voltage regulation [3]. Amathematical modelling of UPFC for steady- comprehensive and systematic approach forstate and small signal (linearize) dynamic studies mathematical modelling of UPFC for steady-statehas been proposed. The other modified linearize and small signal (linearized) dynamic studies hasHeffron-Philips model of a power system been proposed in [4-7]. The other modifiedinstalled with UPFC is presented. For systems linearized Heffron-Philips model of a power systemwhich are without power system stabilizer (PSS), installed with UPFC is presented in [8] and [9]. Forexcellent damping can be achieved via proper systems which are without power system stabilizercontroller design for UPFC parameters. By (PSS), excellent damping can be achieved viadesigning a suitable UPFC controller, an proper controller design for UPFC parameters. Byeffective damping can be achieved. In this designing a suitable UPFC controller, an effectiveresearch the linearize model of synchronous damping can be achieved. It is usual that Heffron-machine (Heffron-Philips) connected to infinite Philips model is used in power system to studybus (Single Machine-Infinite Bus: SMIB) with small signal stability. This model has been used forUPFC is used and also in order to damp LFO, many years providing reliable results [10]. In recentadaptive neuro-fuzzy controller for UPFC is years, the study of UPFC control methods hasdesigned and simulated. Simulation is performed attracted attentions so that different controlfor various types of loads and for different approaches are presented for UPFC control such asdisturbances. Simulation results show good Fuzzy control [11-14], conventional lead-lagperformance of neuro-fuzzy controller in control, Genetic algorithm approach, and Robustdamping LFO. control methods . In this case study, the class of adaptiveKeywords – Neuro-Fuzzy Controller, Low networks that of the same as fuzzy inference systemFrequency Oscillations (LFO), Unified Power in terms of performance is used. The controllerFlow Controller (UPFC), Single Machine-Infinite utilized with the above structure is called AdaptiveBus (SMIB). Neuro Fuzzy Inference System or briefly ANFIS. Applying neural networks has many advantagesI. INTRODUCTION such as the ability of adapting to changes, fault Flexible AC Transmission Systems, called tolerance capability, recovery capability, High-FACTS, got in the recent years a well known term speed processing because of parallel processing andfor higher controllability in power systems by ability to build a DSP chip with VLSI Technology.means of power electronic devices. Several FACTS- To show performance of the designed adaptivedevices have been introduced for various neuro-fuzzy controller, a conventional lead-lagapplications worldwide. The growth of the demand controller that is designed in is used and thefor electrical energy leads to loading the simulation results for the power system including 1560 | P a g e
  2. 2. A.V.Sudhakara Reddy, M. Ramasekhara Reddy, Dr. M. Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1560-1565these two controllers are compared with each other. III.OPERATING PRINCIPLE OF UPFCThe following Section, the model of the power In the presently used practicalsystem including UPFC is presented. The proposed implementation, The UPFC consists of twoAdaptive Neuro-Fuzzy controller and lead-lag switching converters, which in the implementationscompensator are explained in Section III. The considered are voltage source inverters using gateresults of the simulation are finally given in Section turn-off (GTO) thyristor valves, as illustrated in theIV. Finally conclusions are presented in section V. Fig 2.1. These back to back converters labeled “Inverter 1 and “Inverter 2” in the figure areII. MODEL OF THE POWER SYSTEM operated from a common dc link provided by a dcINCLUDING UPFC storage capacitor. UPFC is one of the famous FACTs devices This arrangement functions as an ac to acthat is used to improve power system stability. Fig.1 power converter in which the real power can freelyshows a single machine- infinite-bus (SMIB) system flow in either direction between the ac terminals ofwith UPFC. It is assumed that the UPFC the two inverters and each inverter canperformance is based on pulse width modulation independently generate (or absorb) reactive power(PWM) converters. In figure 1 me, mb and δe, δb are at its own ac output terminal.the amplitude modulation ratio and phase angle ofthe reference voltage of each voltage sourceconverter respectively. These values are the inputcontrol signals of the UPFC. Fig. Basic circuit arrangement of unified power flow controller The basic function of inverter 1 is to supply or absorb the real power demanded by Inverter 2 at the common dc link. This dc link power is converted back to ac and coupled to the transmission line via a shunt-connected transformer.Fig.1 A single machine connected to infinite bus Inverter 1 can also generate or absorb controllablewith UPFC reactive power, if it is desired, and there by it can provide independent shunt reactive compensation As it mentioned previously, a linearized for the line.model of the power system is used in dynamicstudies of power system. In order to consider theeffect of UPFC in damping of LFO, the dynamicmodel of the UPFC is employed; In this model theresistance and transient of the transformers of theUPFC can be ignored. The Linearized state variableequations of the power system equipped with theUPFC can be represented as [8]. Fig2.Principle configuration of an UPFC The UPFC consists of a shunt and a series transformer, which are connected via two voltage source converters with a common DC-capacitor. The DC-circuit allows the active power exchange between shunt and series transformer to control the phase shift of the series voltage. This setup, as shown in Figure 1.21, provides the full controllability for voltage and power flow. The series converter needs to be protected with a Where ΔmE, ΔmB , ΔδE and ΔδB are the Thyristor bridge. Due to the high efforts for thedeviation of input control signals of the UPFC. Also Voltage Source Converters and the protection, anin this study IEEE Type- ST1A excitation system UPFC is getting quite expensive, which limits thewas used. practical applications where the voltage and power flow control is required simultaneously. 1561 | P a g e
  3. 3. A.V.Sudhakara Reddy, M. Ramasekhara Reddy, Dr. M. Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1560-1565IV. CONTROLLER DESIGN These are called normalized firing strengths. Then fI. Lead-Lag Controller Design can be written as As mentioned before, in this study two f = w¯1f1+......+ w20f20 (5)different controllers have been used to damp LFO.The first one is conventional lead-lag controller. It The above relation is linear with respect toconsists of gain block, washout block, lead-lag pi , qi, ri and i=1,…,20. So parameters can becompensator block. The washout block is categorized into 2 sets: set of linear parameters andconsidered as a high-pass filter, with the time set of nonlinear parameters. Now Hybrid learningconstant TW. Without this block steady changes in algorithm can be applied to obtain values ofinput would modify the output. The value of TW is parameters. Hybrid learning algorithm isnot critical and may be in the range of 1 to 20 combination of linear and nonlinear parametersseconds. In this study, the parameters obtained from learning algorithm. Description for learninglead-lag controller design that is presented in, were procedure can be found in . This network is calledused. adaptive by Jang and it is functionally equivalent to Sugeno type of a fuzzy system. It is not a uniqueII. Adaptive Neuro-Fuzzy Controller Design presentation. With regard to the explanations Another controller is adaptive neuro-fuzzy presented and with the help of MATLAB software,controller. In this section, we will present the adaptive neuro-fuzzy controller can be designed.procedure of designing of the adaptive neuro-fuzzy The rules surface for designed controller iscontroller. In this research, the neuro fuzzy shown in figure 3.The membership functions forcontroller has 2 inputs that are Δδ and Δω and it has input variable ∆𝜔 are presented in figure 4.1 output that is f ∈ {∆𝑚E, ∆𝛿 E, ∆𝑚B, ∆𝛿 B }. Foreach input 20 membership functions and also 20rules in the rules base is considered. Figure 5demonstrates the structure of adaptive neuro-fuzzycontroller for a sugeno fuzzy model with 2 inputsand 20 rules. Fig.3 The rules surfaceFig.3 ANFIS architecture for a two-input Sugenofuzzy model with 20 rulesIn Figure 3, a Sugeno type of fuzzy system has therule base with rules such as follows: 1. If Δδ is A1 and Δω is B1 then f1=p1 Δδ+q1Δω+r1. 2. If Δδ is A2 and Δω is B2 then f2=p2 Δδ+q2Δω+r2. 𝜇 𝐴 𝑖  and 𝜇 𝐵 𝑖 are the membership functionsof fuzzy sets Ai and Bi for i=1,…,20. In evaluatingthe rules, we choose product for T-norm (logicaland). Then controller could be designed infollowing steps: Fig.4 The membership functions for input variable1. Evaluating the rule premises: ∆𝜔 𝑤 𝑖 = 𝜇 𝐴 𝑖 (Δ𝛿) 𝜇 𝐵 𝑖 (∆𝜔), i= 1.....20(2) One of the advantages of using neuro-2.Evaluating the implication and the rule fuzzy controller is that we can utilize one of theconsequences: designed controllers for instance Δme controller in 𝑤 1 ∆𝛿,∆𝜔 𝑓1 (∆𝛿,∆𝜔 )+⋯+𝑤 20 (∆𝛿 ,∆𝜔 )𝑓 20 (∆𝛿 ,∆𝜔 ) place of the other controllers. While if we usef(∆𝛿, ∆𝜔) = 𝑤 1(∆𝛿 ,∆𝜔 ) +⋯+𝑤 20(∆𝛿 ,∆𝜔 ) conventional lead-lag controllers, for each controlOr leaving the arguments out parameters, a controller must be designed. 𝑤 𝑓 +⋯+𝑤 20 𝑓20 f = 11 (3) 𝑤 1 +⋯+𝑤 20 V. SIMULATION RESULTSThis can be separated to phases by first defining In this research, two different cases are 𝑤𝑖 w¯i = 𝑤 +⋯+𝑤 studied. In the first case mechanical power and in 1 20(4) the second case reference voltage has step change and deviation in ω (Δω) and deviation in rotor angle 1562 | P a g e
  4. 4. A.V.Sudhakara Reddy, M. Ramasekhara Reddy, Dr. M. Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1560-1565δ (Δδ) is observed. The parameter values of systemare gathered in Appendix. In first case, step changein mechanical input power is studied. Simulationsare performed when mechanical input power has10% increase (ΔPm=0.1 pu) at t=1 s. Simulationresults for different types of loads and controllers (ΔmE, ΔδE, ΔmB, ΔδB ) and step change inmechanical input power are shown in figures 5 to 9. Figure 5.5Generator Parameters :M = 2H = 8.0MJ /MVA , D = 0.0, Td0’ = 5.044SXd = 1.0pu , Xq = 0.6 , Xd „ = 0.3Exciter (IEEE Type ST1): 𝑘 𝐴 = 100, TA = 0.01SReactances: XIE = 0.1 pu , XE = XB =0.1 pu, XBv = 0.3 pu, XE = 0.5 puOperation Condition: Pe = 0.8 pu, Vt = Vb=1.0 puUPFC parameters: mE = 0.4013, mB = 0.0789 ,δE = -85.3478° , δB = -78.2174°DC link: Vdc = 2 pu, Cdc = 1 pu Figure 5.6Results with Lead-Lag controller : Results with Fuzzy controller :Figure 5.1 Figure 6.1Figure 5.2 Figure 6.2Figure 5.3 Figure 6.3Figure 5.4 Figure 6.4 1563 | P a g e
  5. 5. A.V.Sudhakara Reddy, M. Ramasekhara Reddy, Dr. M. Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1560-1565 conventional lead-lag compensator and the proposed neuro-fuzzy controller for the UPFC indicates that the proposed neuro-fuzzy controller has less settling time and less overshoot in comparison with the conventional lead-lag compensator. Hence the performance of system can be improved i.e., another way stability of power system can be improved with fuzzy controller.Figure 6.5 V. CONCLUSIONS With regard to UPFC capability in transient stability improvement and damping LFO of power systems, an adaptive neuro-fuzzy controller for UPFC was presented in this paper. The controller was designed for a single machine infinite bus system. Then simulation results for the system including neuro-fuzzy controller were compared with simulation results for the systemFigure 6.6 including conventional lead-lag controller. Simulations were performed for different kinds of loads. Comparison. showed that the proposed adaptive neuro-fuzzy controller has good ability to reduce settling time and reduce amplitude of LFO. Also we can utilize advantages of neural networks such as the ability of adapting to changes, fault tolerance capability, recovery capability, High- speed processing because of parallel processing andFigure 7. FIS Editor ability to build a DSP chip with VLSI Technology. Fuzzy logic is a convenient way to map an input Fig 5.1 & 6.1 shows angular velocity space to an output space. Mapping input to output isdeviation during step change in mechanical input the starting point for everything. Hence stability ofpower for nominal load (me Controller). Fig 5.2 & the system can be improved.6.2 shows angular velocity deviation during stepchange in mechanical input power for light load (me REFERENCESController). Fig 5.3 & 6.3 shows angular velocity [1] N. G. Hingorani and L. Gyugyi,deviation during step change in mechanical input Understanding FACTS: Concepts andpower for nominal load (δe Controller). Fig 5.4 & Technology of Flexible AC Transmission6.4 shows angular velocity deviation during step System, IEEE Press, 2000.change in mechanical input power for nominal load [2] H.F.Wang, F.J.Swift," A Unified Model(mb Controller). Fig 5.5 & 6.5 shows angular for the Analysis of FACTS Devices invelocity deviation during step change in mechanical Damping Power System Oscillations Partinput power for nominal load (δb Controller). Fig I: Single-machine Infinite-bus Power5.6 & 6.6 shows Response of angular velocity for Systems", IEEE Transactions on Power5% step change in reference voltage in the case of Delivery, Vol. 12, No. 2, April 1997,nominal load (δb Controller). pp.941-946. As it can be seen from figures 5.1 to 5.6, [3] L. Gyugyi, C.D. Schauder, S.L. Williams,lead-lag controller response is not as good as neuro- T.R.Rietman, D.R. Torgerson, A. Edris,fuzzy controller response which shown from figures "The UnifiedPower Flow Controller: A6.1 to 6.6 and also neuro-fuzzy controller decreases New Approach to PowerTransmissionsettling time. In addition maximum overshoot has Control", IEEE Trans., 1995, pp. 1085-decreased in comparison with lead-lag controller 1097.response. In second case, simulations were [4] Wolanki, F. D. Galiana, D. McGillis andperformed when reference voltage has 5% increase G. Joos, “Mid-Point Sitting of FACTS(ΔVref = 0.05 pu) at t=1 s. Figure 10 demonstrates Devices in Transmission Lines,” IEEEsimulation result for step change in reference Transactions on Power Delivery, vol. 12,voltage, under nominal load and for δb Controller. No. 4, 1997, pp. 1717-1722. Consequently simulation results show that [5] M. Noroozian, L. Angquist, M. Ghandari,neuro- fuzzy controller successfully increases and G. Anderson, “Use of UPFC fordamping rate and decreases the amplitude of low optimal power flow control”, IEEE Trans.frequency oscillations. Results comparison between 1564 | P a g e
  6. 6. A.V.Sudhakara Reddy, M. Ramasekhara Reddy, Dr. M. Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 5, September- October 2012, pp.1560-1565 on Power Systems, vol. 12, no. 4, 1997, pp. 1629–1634. [6] A Nabavi-Niaki and M R Iravani. „Steady- state and Dynamic Models of Unified Power Flow Controller (UPFC) for Power System Studies.‟ IEEE Transactions on Power Systems, vol 11, 1996, p 1937. [7] K S Smith, L Ran, J Penman. „Dynamic Modelling of a Unified Power Flow Controller.‟ IEE Proceedings-C, vol 144, 1997, pp.7. [8] H F Wang. „Damping Function of Unified Power Flow Controller.‟ IEE Proceedings- C, vol 146, no 1, January 1999, p 81. [9] H. F. Wang, F. J. Swift, “A Unified Model for the Analysis of FACTS Devices in Damping Power System Oscillations Part I: Single-machine Infinite-bus Power Systems,” IEEE Transactions on Power Delivery, Vol. 12, No. 2, April, 1997, pp. 941-946. [10] P. Kundur,"Power System Stability and Control",McGraw-Hill. [11] M. Banejad, A. M. Dejamkhooy , N. Talebi, " Fuzzy Logic Based UPFC Controller for Damping Low Frequency Oscillations of Power Systems", 2nd IEEE International Conference on Power and Energy, 2008, pp. 85-88. [12] P.K., Dash, S. Mishra, G. Panda, , "Damping multimodal power system oscillation using a hybrid fuzzy controller for series connected FACTS devices", IEEE Trans. on Power Systems, Vol. 15, 2000, pp. 1360 -1366. 1565 | P a g e