jzs-10633

Genetic Algorithm Based Feeder Reconfiguration and Control of Reactive Power for Loss Reduction

Jwan Satei Ra'afat1

1Electrical Department, Faculty of Engineering, Sulaimani University, Kurdistan Region, Iraq

Doi Link/ https://doi.org/10.17656/jzs.10633

Original: 10 October 2016,  Revised: 1 October 2017,  Accepted: 22 November 2017,  Published online: 20 December 2017

Abstarct

Power distribution network consists of a group of radial feeders which can be connected together by several tie-switches and tie-lines. The power loss reduction in the network is a major concern of electric distribution utilities. Among conventional methods, optimal reconfiguration and capacitor placement are two effective methods which can be applied on the network. In this paper a feeder reconfiguration algorithm is build and present for

the purpose of power loss reduction in distribution systems. The methodology developed combine optimization technique. The network reconfiguration problem is formulated as single objective optimization problem with equality and inequality constraints. The proposed solution to this problem is based on a general combinatorial optimization algorithm known as genetic algorithm, and the load flow equations in distribution

system. The GA optimizes the system-switching pattern for losses reduction. Decides the system topology, and considered taken the size and location of capacitor placement in the radial distribution networks. The two selection method used in genetic algorithm, first method roulette wheel

selection and second method tournament selection. Tests show the different in execution time of the program to arrive the best fitness function.

The proposed algorithm has been implemented in technical MATLAB package and tested for practical network (48-bus) which is taken from the distribution system of Palestine Street in Baghdad city. Tests show that GA is suitable algorithm as optimization technique, high accuracy, and avoid local minimum by searching in several regions to arrive to the global optimum solution. Thus, the outcome of the study shows an efficient technique to solve the problem of network reconfiguration for loss reduction in the distribution power system.

Keywords: Genetic Algorithm, Load flows, Capacitor placement, Optimal, reconfiguration, Power, loss reduction

References:

[1] H. Kim, Y. Ko and K. H. Jung "Artificial Neural Network Based Feeder Reconfiguration For Loss Reduction in Distribution System". IEEE Trans. On power delivery, Vol. 8, No. 3, July. (1993).

[2] T. E. Mcdermott, I. Drezga, and R. P. Broadwater, "A Heuristic Nonlinear Constructive Method for Distribution System Reconfiguration". IEEE trans. On power system, Vol. 14, No. 2, May (1999).

[3] M. Kaptan, "Optimization of Number, Location, Size, Control, Type, and Control Setting of Shunt Capacitor on Radial Distribution Feeder". IEEE trans. on Power Apparatus and System, Vol. 103, No. 9, September (1984).

[4] Y. C. Huang, H. T. Yang, and C. L. Huang, "Solving the Capacitor Placement Problem in a Radial Distribution System Using Tabu Search Approach", IEEE Transactions on Power Systems, Vol. 11, No. 4, November (1996).

[5] M. E. Baran and F. F. Wu, "Network Reconfiguration in Distribution Systems For Loss Reduction And Load Balancing". IEEE Trans. on power delivery, Vol. 4,No.2, April (1989). 

[6] K. Nara, A. Shiose, M. Kitagawa,and T. Ishihara, "Implementation of Genetic Algorithm For Distribution Systems Loss Minimum Reconfiguration ". IEEE Trans. on power systems, Vol. 7, No. 3, August 1992.

[7] Q. Zhou, D. Shirmohammadi, and W.-H.E. Liu, "Distribution Feeder Reconfiguration For Operation Cost Reduction". IEEE Trans. on power systems, Vol. 12, No. 2,May (1997).

[8] A. Saad, "Optimal Network Reconfiguration in Electrical Power Distribution System", M. Sc. Thesis (1999).

[9] M. A. Matos, and Paulomelo, "Mulitobjective Reconfiguration For Loss Rreduction And Service Restoration Using Simulated Annealing", http://power.inescn.pt/paulo/paper1.pdf. (1999).

[10] T. Ghose, S. K. Goswami, and S. K. Basu, "Solving Capacitor Placement Problems in Distribution Systems Using Genetic Algorithm". Taylor & Francis, Electric Machines and Power Systems, Vol. 27, pp. 429 – 441. (1999).

[11] A. Augugliaro, L. Dusonchet, M. G. Ippolito, and E. R. Sanseverino, "Minimum Losses Reconfiguration of MV Distribution Networks Through Local Control of Tie-Switches". IEEE Trans. on Power Delivery, Vol. 18, No. 3, July (2003).

[12] R. T. F. Ah King, B. Radha and H. C. S. Rughooputh, "A Fuzzy Logic Controlled Genetic Algorithm Optimal Electrical Distribution Network Reconfiguration". IEEE International Conference on Networking, Sensing & Control Taipei, Taiwan March, (2004).

[13] L. Garcia Santander, and F. Abou Chacra, "Minimal Loss Reconfiguration Based on Simulated Annealing Meta – Heuristic". Proceeding of the 15th International Conference on Electronics, Communications and Computers (Canaille comp 2005).

[14] R. Rajaram, K. Sathish Kumar and N. Rajasekar, "Power system reconfiguration in a radial distribution network for reducing losses and to improve voltage profile using modified plant growth simulation algorithm with Distributed Generation (DG)". Energy Reports Vol. 1, pp. 116–122. (2015).

[15] Manju Mam, Leena G and N.S. Saxena, "Distribution Network Reconfiguration for Power Loss Minimization Using Bacterial Foraging Optimization Algorithm". I.J. Engineering and Manufacturing, Vol. 2, pp. 18-32. (2016) in MECS (http://www.mecs-press.net) DOI: 10.5815/ijem.2016.02.03.

[16] Robert T. F. Ahking, B. Radha, and Harry C.S. Rughooputh, “A Real-Parameter Genetic Algorithm for Optimal Network Reconfiguration". University of Mauritius Reduit, Mauritius, (2003).

[17] R. Taleski, and D. Rajicic, "Distribution Network Reconfiguration for Energy Loss Reduction", IEEE Trans. on power systems, Vol. 12, No. 1, February (1997).

[18] S. Sivanagaraju and T. Ramana, "A Simple Method for Feeder Reconfiguration and Service Restoration of Distribution Network", Taylor & Francis, Vol. 32, No. 9, pp. 883 – 892. (2004).

[19] D. E. Goldberg, "Genetic Algorithms in Search Optimization and Machine Learning". Addision-Wesley Publishing Company, (1989).

[20] M. Mitchell, "An Introduction to Genetic Algorithms", Massachusetts Institute of Technology, England, (1996).

[21] Mitsuo Gen, and Runwei Cheng. "Genetic Algorithm and Engineering Optimization". John Wiley & Sons, Inc, (2000).

[22] H. Fudou, T. Genji, Y.Fukuyama, and Y. Nakanishi, "A Genetic Algorithm For Network Reconfiguration using Three Phase Unbalanced Load Flow". http://homepage2.nifty.com/fukuyama- yoshikazu/Isap97GA%20Lossmin.pdf . (1997).

[23] K. N. Miu, H. D. Chiang, and G. Darling, "Capacitor Placement, Replacement and Control in Large – scale Distribution Systems by a GA – Based Two – Stage Algorithm". IEEE Trans. on Power Systems. Vol. 12, No. 3, (1997).

View Full Text