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Evolutionary Algorithms for Load Frequency Control in Two Area Interconnected Power System
Abstract
A Power system is an interconnected electric network which makes available, electric power generated by the generating plants to the loads through transmission lines. Modern power systems are large, highly complex and organized in the form of regional grids, which are interconnected to facilitate power transfer between areas via tie-lines. Interconnections make the system more reliable, since the power can be borrowed from the neighboring area. It is the responsibility of power generating system to ensure that adequate power is delivered to the load, both reliably and economically. Any electrical system must be maintained at the desired operating level characterized by nominal frequency and voltage profile. Hence, a Power System Control is required to maintain a continuous balance between power generation and load demand. The quality of power supply is affected due to continuous and random changes in load during the operation of the power system. Load Frequency Controller (LFC) play an important role in maintaining constant frequency in order to ensure the reliability of electric power. In order improve the performance and stability of this control loop, PID controllers are normally used. But these fixed gain controllers fail to perform under varying load conditions and hence provide poor dynamic characteristics. Also the conventional PSO based PID controllers will have large settling time, overshoot and oscillations. In order to achieve better dynamic performance, system stability and sustainable utilization of generating systems, PID gains must be well tuned. In this paper, Evolutionary Algorithms (EA) like, Enhanced Particle Swarm Optimization (EPSO), Multi Objective Particle Swarm Optimization (MOPSO), and Stochastic Particle Swarm Optimization (SPSO) are proposed to overcome the premature convergence problem in a standard PSO. Simulation results demonstrate that the proposed controller adapt themselves appropriate to varying loads and hence provide better performance characteristics with respect to settling time, oscillations and overshoot.
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References
Hadi Saadat, “Power System Analysis”, McGraw-Hill, 2nd Edition. New Delhi, 2002.
P.Kundur,”Power System Stability and Control”, Tata McGraw-Hill, 2nd Edition. New Delhi, 2006.
J.Wood, F.Wollenberg,”Power generation, Operation and Control”, John Wiley & Sons (ASIA) Pte Ltd, 2nd Edition. New York, 2003.
D.M.Vinod Kumar, Intelligent Controllers for Automatic Generation Control, Proc. of IEEE region 10 International conference on global connectivity in Energy, Computer, Communication and Control, pp557-574., 1998
C.W.de silva, Intellligent Control:Fuzzy logic applications, CRC press Inc,1995
P.S.R.Murthy, Power System Operation and Control, BSP books Pvt. Ltd., 2009.
M.A. Panduro et al, A comparision of Genetic algorithm, Particel Swarm Optimization and the Differential Evolution methods for the design of Scannable circular antenna arrays, Progress in Electromagnetic Research, Vol.No.13, pp171-186, 2009.
Kennedy.J and Eberhart, R.C., Particle Swarm Optimization, Proceedings of IEEE International conference on Neural Networks, Australia, IV:1942-1948,1995.
Zwe-Lee Gaing, A particle Swarm Optimization approach for optimum design of PID controller in AVR system, IEEE Transactions on Energy Conversion, Vol.19, No.2, pp384-391, 2004
You-Bo Wang, Xin Peng, Ben-Zheng Wei, “A New Particle Swarm Optimization Based Auto-Tuning of PID Controller”, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics,Kunming,12-15, 2008.
Haluk Gozde et al, PSO based Load Frequency Control in a single area power system, University of Pitesti, Scientific Bulletin, Vol.2, No.8, pp106-110, 2008.
D.B. Fogel,”Evolutionary Computation: Toward a new philosophy of machine intelligence”, 2nd Edition. New York: IEEE Press, 2000.
J. Kennedy, R.C. Eberhart,” Particle Swarm Optimization”, Proceedings of IEEE conference on neural networks, vol. 4, pp.1942-1948, 1995.
Shi, Y., and Eberhart, R. C., “Empirical Study of Particle Swarm Optimization,” IEEE Congress on Evolutionary Computation, Piscataway, NewJersey, Vol. 3, pp. 1945-1950, 1999
T.Back, U.Hammel, and H.-P.Schwefel, “Evolutionary Computation: Comments on the History and Current state“, IEEE Transactions on Evolutionary Computation, Vol. 1:1, pp.3-17, 1997
Hongqing Fang, and Long Chen, “ Application of an enhanced PSO algorithm to optimal tuning of PID gains”, Proceedings of the Chinese control and Decision Conference,2009.
R. C. Eberhart and Y. Shi, "Comparing inertia weights and constriction factors in particle swarm optimization," in Proc. of the 2000 Congr. Evol. Comput., San Diego, pp. 84-88, 2000
Shigenori Naka, Takamu Genji, Kenji Miyazato, and Yoshikazu Fukuyama, “Hybrid Particle Swarm Optimization using Constriction Factor Approach”, IEEE Transactions on Energy Conversion, vol. 19, no. 2, 2004.
Andrey Popov, Adel Farag and Herbert Werner, “Tuning of a PID controller using Multi-objective optimization technique”, Proceedings of the 44th IEEE International Conference on Decision and control, Seville, Spain, 2005.
K. Sabahi, A. Sharifi, M. Aliyari, M. Teshnehlab and M. Aliasghary, “Load Frequency Control in Interconnected Power system using Multi-objective PID Controller”, Journal of Applied Sciences 8 (20): 3676-3682, 2008
M. Janga Reddy and D. Nagesh Kumar, “An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design”, Engineering Optimization, Vol.39, No.1, 49-68, 2007.
G. Ramana Moorthy, M. Senthil Arumugam and C.K. Loo, ”Hybrid particle swarm optimization with fine tuning parameters”, International Journal of Bio-inspired computations, vol.1, No.s 1/2 ,2009.
Y.L. Karnavas, D.P. Papadopoulos, “AGC for autonomous power system using combined intelligent techniques”, Electric Power Systems Research, vol. 62, pp 225-239, 2002.
Liang Qin, A. Suganthan and Baskar “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions”, IEEE Trans. Evolutionary Computation, Vol. 10, No. 3, pp.281–295.
Zeng J., Jie J. and Hu J. , “Adaptive particle swarm optimisation guided by acceleration information”, International Conference on Computational Intelligence and Security, Vol. 1, pp.351–355.
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