JCC  Vol.2 No.9 , July 2014
Application of Evolutionary Algorithm for Optimal Directional Overcurrent Relay Coordination
Abstract: In this paper, two Evolutionary Algorithms (EAs) i.e., an improved Genetic Algorithms (GAs) and Population Based Incremental Learning (PBIL) algorithm are applied for optimal coordination of directional overcurrent relays in an interconnected power system network. The problem of coordinating directional overcurrent relays is formulated as an optimization problem that is solved via the improved GAs and PBIL. The simulation results obtained using the improved GAs are compared with those obtained using PBIL. The results show that the improved GA proposed in this paper performs better than PBIL.
Cite this paper: Stenane, N. and Folly, K. (2014) Application of Evolutionary Algorithm for Optimal Directional Overcurrent Relay Coordination. Journal of Computer and Communications, 2, 103-111. doi: 10.4236/jcc.2014.29014.

[1]   (2001) IEEE Recommended Practice for Protection and Coordination of Industrial and Commercial Power Systems. IEEE Standard 242.

[2]   Anderson, P.M. (1999) Power System Protection. McGraw-Hill, New York.

[3]   Tsien, H.Y. (1964) An Automatic Digital Computer Program for Setting Transmission Line Directional Overcurrent Relays. IEEE Transactions on Power Apparatus and Systems, 83, 1048-1053.

[4]   Albrecht, R.E., et al. (1964) Digital Computer Protective Device Co-Ordination Program I-General Program Description. IEEE Transactions on Power Apparatus and Systems, 83, 402-410.

[5]   Urdaneta, A.J. et al., (1988) Optimal Coordination of Directional Overcurrent Relays in Interconnected Power Systems. IEEE Transactions on Power Delivery, 3, 903-911.

[6]   Birla, D., et al., (2005) Time-Overcurrent Relay Coordination: A Review. International Journal of Emerging Electric Power Systems, 2.

[7]   Hussain, M.H., et al. (2013) Optimal Overcurrent Relay Coordination: A Review. Procedia Engineering, 53, 332-336.

[8]   Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Pub. Co., Reading.

[9]   Haupt, R.L. and Haupt, S.E. (2004) Practical Genetic Algorithms. Wiley-Interscience, Hoboken.

[10]   So, C.W., et al., (1997) Application of Genetic Algorithm to Overcurrent Relay Grading Coordination. In: Proceedings of the 4th International Conference on Advances Power System Control, Operation and Management, Hong Kong, 283-287.

[11]   Kavehnia, F., et al (2006) Optimal Coordination of Directional Overcurrent Relays in Power System Using Genetic Algorithm. In: Proceedings of the 41st International Universities Power Engineering Conference, 824-827.

[12]   Uthitsunthorn, D. and Kulworawanichpong, T. (2010) Optimal Overcurrent Relays Coordination Using Genetic Algo- rithm. Advances in Energy Engineering (ICAEE) Conference, 162-165.

[13]   Razavi, F., et al., (2008) A New Comprehensive Genetic Algorithm Method for Optimal Overcurrent Relays Coordination. Electric Power Systems Research, 78, 713-720.

[14]   Coello Coello, C.A. (2002) Theoretical and Numerical Constraint-Handling Techniques used with Evolutionary Algo- rithms: A Survey of the State of the Art. Computer Methods in Applied Mechanics and Engineering, 191, 1245-1287.

[15]   Noghabi, A.S., et al., (2009) Considering Different Network Topologies in Optimal Overcurrent Relay Coordination Using a Hybrid GA. IEEE Transactions on Power Delivery, 24, 1857-1863.

[16]   Bedekar, P.P. and Bhide, S.R. (2011) Optimum Coordination of Overcurrent Relay Timing Using Continuous Genetic Algorithm. Expert Systems with Applications, 38, 11286-11292.

[17]   So, C.W. and Li, K.K. (2000) Overcurrent Relay Coordination by Evolutionary Programming. Electric Power Systems Research, 53, 83-90.

[18]   So, C.W. and Li, K.K. (2004) Intelligent Method for Protection Coordination. In: Proceedings of the 2004 IEEE International on Electric Utility Deregulation, Restructuring and Power Technologies, 378-382.

[19]   Thangaraj, R., et al. (2012) Overcurrent Relay Coordination by Differential Evolution Algorithm. In: IEEE International Conference on Power Electronics, Drives and Energy Systems.

[20]   Thangaraj, R., Pant, M. and Abraham, A. (2010) New Mutation Schemes for Differential Evolution Algorithm and Their Application to the Optimization of Directional Over-Current Relay Settings. Applied Mathematics and Computa- tion, 216, 532-544.

[21]   Thangaraj, R., Pant, M. and Deep, K. (2010) Optimal Coordination of Over-Current Relays Using Modified Differential Evolution Algorithms. Engineering Applications of Artificial Intelligence, 23, 820-829.

[22]   Moirangthem, J., Krishnanand, K.R., Dash, S.S. and Ramaswam, R. (2013) Adaptive Differential Evolution Algorithm for Solving Non-Linear Coordination Problem of Directional Overcurrent Relays. IET Generation, Transmission and Distribution, 7, 329-336.

[23]   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.

[24]   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.

[25]   Folly, K.A. and Venayagamoorthy, G.K. (2009) A Real-Time Implementation of a PBIL Based Stabilizing Controller for Synchronous Generator. Proceedings of the IEEE Industry Applications Society Annual Conference.

[26]   Folly, K.A. and Venayagamoorthy, G.K. (2009) Effects of Learning Rate on the Performance of the Population Based Incremental Learning Algorithm. International Joint Conference on Neural Networks, 861-868.

[27]   Folly, K.A. (2012) Population Based Incremental Learning Algorithm with Adaptive Learning Rate Strategy. Proceedings of the 3rd International Conference on Advanced Neural Networks, International Joint Conference on Neural Nin Swarm Intelligence (ICSI’12), Vol. 1, 11-20.

[28]   Folly, K.A. (2007) Robust Controller Based on a Combination of Genetic Algorithms and Competitive Learning. Proceedings of the 2007 International Joint Conference on Neural Network (IJCNN).

[29]   Folly, K.A. (2006) Design of Power System Stabilizer: A Comparison between Genetic Algorithms (GAs) and Population-Based Incremental Learning (PBIL). Proceedings of the 2006 IEEE Power and Energy Society, General Meeting.

[30]   Power system Test Case Archive.