International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN Volume 3, Issue 7, October 2014

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1 1044 OPTIMIZATION AND SIMULATION OF SIMULTANEOUS TUNING OF STATIC VAR COMPENSATOR AND POWER SYSTEM STABILIZER TO IMPROVE POWER SYSTEM STABILITY USING PARTICLE SWARM OPTIMIZATION TECHNIQUE Abishek Paliwal 1, Kapil Parikh 2, Raunak Jangid 3 1 MTECH Student, Power system, SITE, Nathdwara 2 Assistant Professor, EE Department, SITE, Nathdwara 3 Assistant Professor, EE Department, SITE, Nathdwara ABSTRACT In this paper, the tuning of a power system stabilizer (PSS) and a static var compensator () based at the same time with Power system stability enhancement investigated in detail. The proposed damping stabilizer s coordination with the internal voltage regulators has also been considered. The formulation of a design problem is done as an optimization problem with a time-domain objective function, which is based simulation. For optimal parameters, the optimization technique utilized here is real-coded particle swarm optimization (PSO) technique. Under different disturbance like balance fault, unbalance fault, small disturbance, on a weekly connected power system the proposed stabilizers are tested. The nonlinear simulation results are presented to demonstrate the proposed control schemes effectiveness and ruggedness over a broad range of different disturbance conditions. Further, the proposed design approach is found to be robust and improves stability efficiently even under effect of signal transmission delay. Keywords- FACTS, PSS, PSO, I. INTRODUCTION When relatively weak tie lines are employed for the interconnection of large power systems, low frequency oscillations are observed. If no adequate damping is available, these oscillations may sustain and grow to result in system separation. In the industry to damp out power system oscillations use of Power System Stabilizers (PSS) are now normal. But, even in some operating conditions this device does not perform effectively and is not able to provide adequate damping. Therefore, other effective options are required apart from PSS. With the flexible ac transmission System (FACTS) technology advent, the role of shunt FACTS devices is important in the reactive power flow control in the power network and hence the system voltage fluctuations and stability. Among FACTS family, Static var compensator () is one such member, which is connected in shunt with the system. Apart from injecting reactive power to support bus voltage, is also capable to improve the power system stability. When a is present in a Power system to support the bus voltage, a additional damping could be designed for the regulation of the bus voltage so as to improve system oscillations damping. The interaction between PSS and -based may upgrade or degrade the damping of certain modes of rotors oscillating modes. Conventional power system stabilizers namely: the pole placement technique, phase compensation/root locus technique, residue compensation, and also the modern control theory. Unfortunately, the conventional methods are time consuming and require heavy computation burden and slow convergence. In addition, the process of search is susceptible to be trapped in local minima and the solution obtained may not be optimal. The evolutionary methods constitute an approach to search for the optimum solutions via some form of directed random search process. A relevant evolutionary methods characteristic is that they search for solutions without previous problem knowledge. Recently, particle swarm optimization (PSO) appeared as a promising evolutionary technique for handling the optimization problems. Particle swarm optimization (PSO) has been popular in academia and the industry mainly because of its intuitiveness, ease to implement, and the ability of solving effectively the highly nonlinear, mixed integer problems of optimization that are typical of complex engineering systems. In view of the above, this paper proposes to use particle swarm optimization technique for the simultaneous tuning of PSS and -based. II. OBJECTIVE FUNCTION t=t J = sim w. t. dt (1) t=0 Where, w is the speed deviation and t sim is the time range of simulation. For -based K min S K S K S (2) T min 1S T 1S T 1S (3) T min 2S T 2S T 2S (4) T min 3S T 3S T 3S (5) T min 4S T 4S T 4S (6) For voltage regulator KP min VR KP VR KP VR (7) KI VR KI VR KI VR (8)

2 1045 For power system stabilizer K PS K PS K PS (9) T min 1P T 1P T 1P (10) T min 2P T 2P T 2P (11) T min 3P T 3P T 3P (12) T min 4P T 4P T 4P (13) For calculation of objective function, the power system model time-domain simulation performed for the simulation period. It is aimed to minimize to objective function in order to improve the system response in terms of the settling time and over shoots. III. SMIB SYSTEM WITH PSS AND To design PSS and -based is presented, their performance, a single machine infinite-bus power system with is show in the figure1. The generator is equipped with hydraulic turbine and governor (HTG), excitation system and power system stabilizer Fig. 1 Single machine infinite bus system with PSS and A. Overview of Power System Stabilizer (PSS) and Its Control System The PSS primary function is to add damping to the generator rotor oscillations. It is carried out by controlling its excitation utilizing auxiliary stabilizing signal. The stabilizer must generate an electrical torque component which is in phase with the rotor speed deviation to give damping. Besides giving terminal voltage fast control, excitation systems with high performance are important for sustaining modern synchronous generators steady state and transient stability. In power systems, it is seen that fast acting exciters with high gain AVR can add to oscillatory instability. Fig. 2: Structure of Power system stabilizer (PSS). The PSS includes an amplification block, a signal washout block, a lead-lag block, and a sensor delay block. The lead lag block gives a proper phase lead characteristic to compensate for the phase lag between the generator electrical torque and the exciter input. Either speed deviation or active power can be the PSS input signal; the PSS structure is shown in Figure 2 Fig. 3: Basic block diagram of PSS Fig.3 basic block diagram of PSS and shows mathmatical equation given below V 2 = pt w (K 1+pT STAB r ) (14) w We can rewrite above equation in state variables. p V 2 = a 51 r + a 52 δ + a 53 ψ fd + K STAB a 51 = K STAB a 11 a 52 = K STAB a 12 a 53 = K STAB a 13 a 55 = 1 T w a 54 = a 56 = 0 p V s = a 61 r + a 62 δ + a 63 ψ fd + a 64 V c + 2H T m(15) a 65 V 2 + a 66 V s + T 1 T 2 K STAB 2H T m (16) B. Overview of Static VAR compensator () and its control system Fig.4Block provides the proper phase-lead characteristics to compensate for the phase lag between the input and output signals. The signal washout block is high pass filter with the time constant T WS, high enough to allow the input signal to pass unchanged. Fig.4 Structure of the based lead-leg damping Without it, steady changes in speed would modify the V S.It allows s to respond only changes in speed. Fig.5 Block diagram of the

3 1046 Fig.5 show the block diagram of the shows mathmatical equation given below B L = 1 ( B T L t + B LO + K U B t (17) The electric equation for the equation the SMIB are V E = x E = V S x L2 (B L t B C )+1 x L2 x L2 (B L t B C )+1 Unlike the traditional methods, the solutions (19) quality of proposed approach does not rely on the x ds = x T + x d + (x L1 + x E ) initial population. Concept of modification of a (20) (4.62) searching point by PSO is shown in fig.5 I d = E q V E cos δ x ds k+1 (21) (21) x i (4.63) I q = V E sin δ x ds (22) (4.64) E q = E q + (x d x d )I d (23) P e = E q I q (24) (4.66) C. Particle Swarm Optimization technique Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Ebehart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evaluations operator such as cross over and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO has been successfully applied in many areas: function optimization, artificial neural networking training, fuzzy system control and other areas where GA can be applied. The PSO technique conducts searches using a population of particles, corresponding to individuals. Each particle represents a candidate solution to the problem at hand. In a PSO system particles change their positions by flying around in a multidimensional search space until a relatively unchanged position has been encountered or until computational limitations are exceed. The advantages of PSO over other traditional optimization techniques can be summarized as follows: PSO is a proportional based search algorithm. This property ensures PSO to be less susceptible to getting trapped on local minima. PSO uses payoff information to guide search in the problem space. Therefore PSO can easily deal with non-differentiable objective functions.. PSO uses probabilistic transition rule and nondeterministic rules. Hence, PSO is a kind of stochastic optimization algorithms that can search a complicated and uncertain area. This makes PSO more flexible and robust than conventional methods. Unlike GA and other heuristic algorithms, PSO (18) has the flexibility to control (4.60) the balance between the global and local exploration of search space. k x i k k+1 Gbest Pbest Pbest i Gbest i Fig. 5: Concept of Modification of a Searching Point by PSO Where x k : Current position, x k+1 : Modified position, V k : Current velocity, V k+1 : Modified velocity, Pbest : Velocity based on Pbest, Gbest : Velocity based on Gbest For each particle, at the current time step, a record is kept of the position, velocity and the best position found in the search space so far. Each particle tries to modify its position using the following information: The current positions The current velocities The distance between the current position and Pbest The distance between the current position and Gbest IV. MATLAB/SIMULIK IMPLEMENTATION MODEL OF THE SMIB SYSTEM WITH A AND PSS Fig.6 shows the MATLAB/SIMULINK diagram of the SMIB system with PSS and.the power system consists of a synchronous generator connected to an infinite bus via step up transformer, followed by a double circuit transmission line. The generator was equipped with a hydraulic turbine and governor (HTG), excitation system and PSS. Double circuit line and fault at the middle of the one line. Different disturbances apply in the system as balance fault, unbalance fault, small disturbance, signal transmission delay on 75ms, 50ms, 25ms and obtain different graph and performance with and without and PSS

4 1047 L-L-G faults, each of 5-cycle durations at the infinitebus terminal at t=1.0 sec. (i) Speed Deviation in SMIB under an L-G Fault in the Transmission Line without and with and PSS Controller. Fig.8 shows graph speed deviation under L-G fault disturbance at 1 sec. Comparison and analysis three cases get less settling time and better optimized parameter when system use with and PSS tuned by PSO. Fig. 6 MATLAB/SIMULIK Model of the SMIB System with a and PSS Case-1 Balance Fault A. Three Phase Fault Disturbance The behavior of the proposed is verified at three phase fault disturbance. A 5 cycle, 3-phase fault is applied at the middle of the line at t=1.0 sec. The original system is restored upon the fault clearance (i)speed Deviation in SMIB under a Three Phase Fault in the Transmission Line without and with and PSS Controller. Fig.7 shows graph speed deviation under three phase fault disturbance at 1 sec. Comparison of three cases we get less settling time and better optimized parameter when system use with and PSS tuned by PSO. Fig. 8 speed deviation under an L-G fault in the transmission line without and with and PSS (ii) Speed Deviation in SMIB under an L-L-G Fault in the Transmission Line without and with and PSS Controller. Fig.9 shows graph speed deviation under L-L-G fault disturbance at 1 sec. Comparison and analysis three cases get less settling time and better optimized parameter when system use and PSS tuned by PSO. Fig. 7 speed deviation under a 3-phase fault in the transmission line without and with and PSS Case-2 Unbalance Fault B. L-G Fault, L-L-G Fault The effectiveness of the proposed on unbalanced fault is also examined by applying selfclearing type unsymmetrical fault, namely L--G and Fig. 9 speed deviation under an L- L-G fault in the transmission line without and with and PSS Case-3 Small Disturbance C. Mechanical Torque Input, Reference Voltage Setting The effectiveness of the proposed is also tested under small disturbance namely mechanical torque input and reference voltage setting. The mechanical power input and reference voltage setting to the generator is increased by 10% at t=1.0 s. The original system is restored upon the fault clearance. (i) Speed Deviation in SMIB under a Mechanical Torque Input in the Transmission Line without and with and PSS Controller Fig.10 shows graph speed deviation under mechanical torque input at 1 sec. Comparison and analysis three cases get less settling time and better

5 1048 optimized parameter when system use with and PSS tuned by PSO. Fig. 10 speed deviation under an L-G fault in the transmission line without and with and PSS (ii) Speed Deviation in SMIB under a reference voltage setting in the Transmission Line without and with and PSS Controller Fig.11 shows graph speed deviation under reference voltage setting at 1 sec. Comparison and analysis three cases get less settling time and better optimized parameter when system use with and PSS tuned by PSO. Fig. 11 speed deviation under reference voltage setting in the transmission line without and with and PSS Case-4 Effect of Small Transmission Delay To study the effect of variation in signal transmission delay on the performance of, The performance of the proposed is hardly affected by the signal transmission delay. (i) Speed Deviation Response for Effect of Signal Transmission Delay Fig.12 shows speed deviation response for effect of signal transmission delay. Their three graph remote signal with 75 ms delay, remote signal with 50 ms delay, remote signal with 25 ms delay. The performance of the proposed is hardly affected by the signal transmission delay. Fig.12 speed deviation under an effect of signal transmission delay V. COMPARISON OF DIFFERENT PARAMETERS WITH CPSS AND PSO TUNED OPTIMIZED PARAMETERS A. Table 1 Balance fault parameter of and PSS with CPSS and combined and PSS Controller tuned by PSO Parameters CPSS PSO Optimized Parameters K T T T T K P K I PSS K PS T 1P T 2P T 3P 3 1 T 4P B.Table-2 Unbalance Fault of and PSS with CPSS and combined and PSS Controller tuned by PSO Parameters CPSS PSO Optimized Parameters L-G Fault L-L-G Fault K T T T T K P K I PSS K PS T 1P T 2P T 3P T 4P C. Table-3 Small disturbances of and PSS with CPSS and combined and PSS Controller tuned by PSO Parameters CPSS PSO Optimized Parameters Mechanical Torque Input Reference Voltage Setting K T

6 1049 T T T K P K I PSS K PS T 1P T 2P T 3P T 4P So studied different parameters of the system and performance increase when we use combined and PSS Controller tuned by PSO use in the system. The different parameters are improving the system. VI. CONCLUSION In this paper, power system stability enhancement by and power system stabilizer is presented. For the proposed design problem, a non-liner simulation-based objective function to increase the system damping was developed. The effectiveness of the proposed, for and power system stability improvement, is demonstrated for single machine infinite-bus subjected to various disturbances as balance fault, unbalance fault, small disturbance. The system is tested on MATLAB/SIMULINK and the results verify the proposed work with CPSS and combined, PSS tuned by PSO. REFERENCES [1] Salma Keskes, Wissem Bahloul and Mohamed Ben Ali Kammoun, Improvement of Power System Stability by Static Var Compensator and Tuning Employing Genetic Algorithm, International Journal of Modern Nonlinear Theory and Application, Vol. 3, pp , [2] Rajendra prasad Narne and P. C. Panda, PSS with Multiple FACTS Controllers Coordinated Design and Real-Time Implementation Using Advanced Adaptive PSO, International Science Index International Journal of Electrical, Robotics, Electronics and Communications Engineering, Vol:8 No: 1, pp , [3] Lod Tapin and Dr. Ram Krishna Mehta, Overview and Literature Survey of Power System Stabilizer In Power Systems, International Journal of Engineering Research and Development, Vol.10, Issue 6, PP.60-71, June [4] Rasool Feiz Kerendian, Hamid Lesani and Javad Olamaei, Improving power system stability by using Dynamic Particle Swarm Optimization Algorithm, Journal of Advanced & Applied Sciences, Vol.2, pp.1-6, [5] Ali, E. S and Abd-Elazim, Stability Enhancement of Multi machine Power System via New Coordinated Design of PSSs and, WSEAS Transactions on Systems, Vol. 13, pp , [6] Ravi Kumar Sahu and and Nitin Saxen, Power Quality Enhancement of Power Transmission System Using Static Var Compensator with PID Controller,International Journal on Emerging Technologies,Vol. 4,pp , [7] E. S. Ali and S. M. Abd-Elazim, Bacteria Foraging: A New Technique for Optimal Design of FACTS Controller to Enhance Power System Stability,Wseas Transactions On Systems,, Vol. 12,Issue-1, pp.42-52, January [8] Ravi Kumar Sahu and Nitin Saxena, Dynamic Performance of Power Transmission system improvement using Static Var Compensator, International Journal of Electrical, Electronics and Computer Engineering,Vol.2,Issue2pp ,2013. [9] Ali, E. S. and Abd-Elazim, S. M, Statistical Assessment of New Coordinated Design of PSSs and via Hybrid Algorithm, International Journal of Engineering and Advanced Technology, Vol.2, Issue-3, pp ,february [10] Sidhartha Panda and C. Ardil, Real-Coded Genetic Algorithm for Robust Power System Stabilizer Design International Science Index International Journal of Electrical, Robotics, Electronics and Communications Engineering, Vol.5, No: 8,pp.51-59, 2011.