An immune-based optimization method to capacitor placement in a radial distribution system

An immune algorithm (IA) based optimization approach for solving the capacitor placement problem is proposed in this paper. In the capacitor placement problem, those practical capacitor operating constraints, load profiles, feeder capacities and allowable voltage limits at different load levels are all considered while the investment cost and energy loss are minimized. In the proposed method, objective functions and constraints are represented as antigens. Through the genetic evolution, an antibody that most fits the antigen becomes the solution. In this IA computation, an affinity calculation process is also embedded to guarantee the diversity. The process stagnation can be thus better prevented. The proposed method has been applied to a test system and the results are compared with other published techniques.

[1]  Y. G. Bae,et al.  Analytical Method of Capacitor Allocation on Distribution Primary Feeders , 1978, IEEE Transactions on Power Apparatus and Systems.

[2]  Ching-Lien Huang,et al.  Application of genetic-based neural networks to thermal unit commitment , 1997 .

[3]  M. E. Baran,et al.  Optimal capacitor placement on radial distribution systems , 1989 .

[4]  J. Chun,et al.  Shape optimization of electromagnetic devices using immune algorithm , 1997 .

[5]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[6]  Philip G. Hill,et al.  Power generation , 1927, Journal of the A.I.E.E..

[7]  O. T. Tan,et al.  Neural-net based real-time control of capacitors installed on distribution systems , 1990 .

[8]  Shyh-Jier Huang,et al.  Genetic-based multilayered perception for Taiwan power system short-term load forecasting , 1996 .

[9]  Yahia Baghzouz,et al.  Effects of nonlinear loads on optimal capacitor placement in radial feeders , 1991 .

[10]  T. Fukuda,et al.  The immune mechanism, adaptation, learning for the multi agent system , 1994, ETFA '94. 1994 IEEE Symposium on Emerging Technologies and Factory Automation. (SEIKEN) Symposium) -Novel Disciplines for the Next Century- Proceedings.

[11]  M. Kaplan.  Optimization of Number, Location, Size, Control Type, and Control Setting of Shunt Capacitors on Radial Distribution Feeders , 1984, IEEE Transactions on Power Apparatus and Systems.

[12]  Shesha H. Jayaram,et al.  Capacitor placement in distribution systems using heuristic search strategies , 1997 .

[13]  Hernando Dura,et al.  Optimum Number, Location, and Size of Shunt Capacitors in Radial Distribution Feeders A Dynamic Programming Approach , 1968 .

[14]  Ching-Tzong Su,et al.  A new fuzzy-reasoning approach to optimum capacitor allocation for primary distribution systems , 1996, Proceedings of the IEEE International Conference on Industrial Technology (ICIT'96).

[15]  N. K. Jerne,et al.  The immune system. , 1973, Scientific American.

[16]  Jin-Cheng Wang,et al.  Optimal capacitor placements in distribution systems. I. A new formulation and the overall problem , 1990 .

[17]  M.A. El-Sharkawi,et al.  Ancillary techniques for neural network applications , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[18]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[19]  Anil Pahwa,et al.  Optimal selection of capacitors for radial distribution systems using a genetic algorithm , 1994 .

[20]  Yoshiki Uchikawa,et al.  Fault diagnosis of plant systems using immune networks , 1994, Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems.

[21]  J. J. Grainger,et al.  Capacity Release by Shunt Capacitor Placement on Distribution Feeders: A New Voltage-Dependent Model , 1982, IEEE Power Engineering Review.