Self-Adaptive DE Applied to Controller Design

K. A. Folly, T. Mulumba

Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa.

DOI: 10.4236/jcc.2014.29007                

Abstract

Adequate damping is necessary to maintain the security and the reliability of power systems. The most-cost effective way to enhance the small-signal of a power system is to use power system controllers known as power system stabilizers (PSSs). In general, the parameters of these controllers are tuned using conventional control techniques such as root locus, phase compensation techniques, etc. However, with these methods, it is difficult to ensure adequate stability of the system over a wide range of operating conditions. Recently, there have been some attempts by researchers to use Evolutionary Algorithms (EAs) such as Genetic Algorithms (GAs), Particle Swarm Optimization, Differential Evolution (DE), etc., to optimally tune the parameters of the PSSs over a wide range of operating conditions. In this paper, a self-adaptive Differential Evolution (DE) is used to design a power system stabilizer for small-signal stability enhancement of a power system. By using self-adaptive DE, the control parameters of DE such as the mutation scale factor F and cross-over rate CR are made adaptive as the population evolves. Simulation results are presented to show the effectiveness of the proposed approach.

Keywords

DE, Power System Stabilizer, Self-Adaptive DE, Small-Signal Stability

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Mitchell, M. (1996) An Introduction to Genetic Algorithms. The MIT Press.
[2]	Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley.
[3]	Abdel, Y.L., Abido, M.A. and Mantawy, H. (1999) Simultaneous Stabilization of Multimachine Power Systems via Genetic Algorithms. IEEE Transactions on Power Systems, 14, 1428-1438. http://dx.doi.org/10.1109/59.801907
[4]	Man, K.F., Tang, K.S. and Kwong, S. (1996) Genetic Algorithms: Concepts and Applications. IEEE Transactions on Industrial Electronics, 43, 519-534. http://dx.doi.org/10.1109/41.538609
[5]	Kaveshgar, N. and Huynh, N. (2012) An Efficient Genetic Algorithm for Solving the Quay Crane Scheduling Problem. Expert Systems with Applications, 39, 13108-13117.
[6]	Li, D., Das, S., Pahwa, A. and Deb, K. (2013) A Multi-Objective Evolutionary Approach for Generation. Expert Systems with Applications, 40, 7647-7655.
[7]	Mühlenbein, H. and Schlierkamp-Voosen, D. (1994) The Science of Breeding and its Application to the Breeder Genetic Algorithm-BGA. IEEE Transactions on Evolutionary Computation, 1, 335-360.
[8]	Baluja, S. (1994) Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning. Carnigie Mellon University, Technical Report CMU-CS-94-163.
[9]	Ho, S.L. and Yang, S. (2010) A Population-Based Incremental Learning Method for Robust Optimal Solutions. IEEE Transactions on Magnetics, 46, 3189-3192. http://dx.doi.org/10.1109/TMAG.2010.2043650
[10]	Folly, K.A. (2013) An Improved Population-Based Incremental Learning Algorithm. International Journal of Swarm Intelligence Research (IJSIR), 4, 35-61. http://dx.doi.org/10.4018/jsir.2013010102
[11]	Folly, K.A. and Venayagamoorthy, G.K. (2009) Effects of Learning Rate on the Performance of the Population Based Incremental Learning Algorithm. In: Neural Networks, International Joint Conference on Neural Networks, 861-868.
[12]	Folly, K.A. (2011) Performance Evaluation of Power System Stabilizers Based on Population-Based Incremental Learning. International Journal of Electrical Power & Energy Systems, 33, 1279-1287. http://dx.doi.org/10.1016/j.ijepes.2011.05.004
[13]	Folly, K.A. and Venayagamoorthy, G.K. (2009) A Real-Time Implementation of a PBIL Based Stabilizing Controller for Synchronous Generator. IEEE Industry Applications Society Annual Conference.
[14]	Folly, K.A. (2012) Population Based Incremental Learning Algorithm with Adaptive Learning Rate Strategy. In The Proceedings of the 3rd International Conference on advanced Neural Networks, International Joint Conference on Neural Nin Swarm Intelligence (ICSI’12), 1, 11-20.
[15]	Folly, K.A. (2007) Robust Controller Based on a Combination of Genetic Algorithms and Competitive Learning. In: Proceedings of the 2007 International Joint Conference on Neural Network (IJCNN), Orlando, Florida. http://dx.doi.org/10.1109/IJCNN.2007.4371446
[16]	He, N., Xu, D. and Huang, L. (2009) The Application of Particle Swarm Optimizer to Passive and Hybrid Active Power Filter. IEEE Transactions on Industrial Electronics, 56, 2841-2851. http://dx.doi.org/10.1109/IJCNN.2007.4371446
[17]	Shayeghi, H., Safari, A. and Shayanfar, H.A. (2008) Multimachine Power System Stabilizers Design Using PSO Algorithm. International Journal of Electrical Power & Energy Systems, 1, 226-233.
[18]	Chan, K.Y., Dillon, T.S. and Chang, E. (2013) Intelligent Particle Swarm Optimization for Short-Term Traffic Flow Forecasting Using on Road Sensor Systems. IEEE Transactions on Industrial Electronics, 60, 4714-4725.
[19]	Price, K., Storn, R. and Lampinem, J. (2005) Differential Evolution: A Practical Approach to Global Optimization. Springer-Verlag, Berlin, Heidelberg.
[20]	Mulumba, T., Folly, K.A. and Malik, O.P. (2011) Tuning of PSS Parameters Using Differential Evolution. Proceedings of the 20th Southern African Universities’ Power Engineering Conference (SAUPEC 2011), Cape Town, 13-15 July 2011.
[21]	Fan, Z., Liu, J., S?rensen, T. and Wang, P. (2009) Improved Differential Evolution Based on Stochastic Ranking for Robust Layout Synthesis of MEMS Components. IEEE Transactions on Industrial Electronics, 56, 937-948. http://dx.doi.org/10.1109/TIE.2008.2006935
[22]	Zhang, J. and Sanderson, A.C. (2009) Adaptive Differential Evolution: A Robust Approach to Multimodal Problem Optimization. Springer, Berlin. Heidelberg. http://dx.doi.org/10.1007/978-3-642-01527-4
[23]	Islam, S.K.M., Das, S., Ghosh, S., Roy, S. and Suganthan, P.N. (2012) An Adaptive Differential Evolution Algorithm with Novel Mutation and Crossover Strategies for Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42, 482-500.
[24]	Brest, J., Greiner, S., Bo?kovié, B., Mernik, M. and ?umer, V. (2006) Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation, 10, 646-657. http://dx.doi.org/10.1109/TEVC.2006.872133
[25]	Lui, J. and Lampinen, J. (2005) A Fuzzy Adaptive Differential Evolution Algorithm. Soft Computing, 9, 448-462. http://dx.doi.org/10.1007/s00500-004-0363-x
[26]	Suganthan, P.N. and Quin, A.K. (2005) Self-Adaptive Differential Evolution Algorithm for Numerical Optimization. In: Congress on Evolutionary Computation, 1785-1791
[27]	Tvrdik, J. (2009) Adaptation in Differential Evolution: A Numerical Comparison. Applied Soft Computing, 9, 1149- 1155. http://dx.doi.org/10.1016/j.asoc.2009.02.010
[28]	Qin, A.K., Huang, V.L. and Suganthan, P.N. (2009) Differential Evolution Algorithm with Strategy Adaptation for Global Numerical Optimization. IEEE Transactions on Evolutionary Computation, 13, 398-417. http://dx.doi.org/10.1109/TEVC.2008.927706
[29]	Kundur, P. (1994) Power System Stability and Control. McGraw-Hill, Inc.
[30]	Rogers, G. (1999) Power System Oscillations. Kluwer Academic, Boston.