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Neural Network Optimal Power Flow(NN-OPF) based on IPSO with Developed Load Cluster Method
Abstract:An Optimal Power Flow based on Improved Particle Swarm Optimization (OPF-IPSO) with Generator Capability Curve Constraint is used by NN-OPF as a reference to get pattern of generator scheduling. There are three stages in Designing NN-OPF. The first stage is design of OPF-IPSO with generator capability curve constraint. The second stage is clustering load to specific range and calculating its index. The third stage is training NN-OPF using constructive back propagation method. In training process total load and load index used as input, and pattern of generator scheduling used as output. Data used in this paper is power system of Java-Bali. Software used in this simulation is MATLAB.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331399Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1620
 Sudhakaran, M., Palanivelu,T.G., "GA and PSO culled hybridtechnique for economic dispatch problem with prohibited operating zones", Journal of Zhejiang University, ISSN 1673-565X, pp. 896 - 903, 2007.
 Pablo, E., Juan, M.R., "Optimal Power Flow Subject to Security Constraints Solved With a Particle Swarm Optimizer", IEEE TransactionsOn Power Systems, Vol. 23, No. 1, pp. 33 - 40, 2008.
 Gaing, Z.L., Particle swarm optimization to solving the economic dispatch considering the generator constrains, IEEE Trans. On Power System, Vol 18. No. 3, pp. 1187 - 1195, 2003.
 Zimmerman,D. Ray, Murilloa E. Carlos, User's Manual A Matlab Power System Simulation Package, Version 3.2 - September 21, PSERC, 2007.
 Boukir, T., Labdani, R., "Economic power dispatch of power system with pollution control using multiobjective particle swarm optimization", University of Sharjah Journal of Pure & Applied Sciences, Vol.4. No..2, pp. 57 - 73, 2007.
 Wang, C.R., Yuan, H.J., "A modified particle swarm optimization algorithm and its application in optimal power flow problem", Proceedings of the fourth International Conference on machine learning and Cybernetics, Guangzhou, 2005.
 Balci, H.H, Valenzuela, J.F., "Scheduling electric power generators using particle swarm optimization combined with the lagrangian relaxation method", AMCS Appl.Math.Comput.Sci, Vol.14. No. 14, pp. 411 - 421, 2004.
 Kumari, M.S., Sydulu, M., "An Improved Evolutionary Computation Technique for Optimal Power Flow Solution", International Journal of Innovations in Energy Systems and Power, Vol. 3, no. 1, pp. 32 - 45, 2008.
 Younes,M., Rahliga,M., "GA Based Optimal Power Flow Solutions", Electrical & Instrumentation Engineering Department, Thapar University, 2008.
 Piccolo, A., Vaccaro, A., "Fuzzy Logic Based Optimal Power Flow Management in Parallel Hybrid Electric Vehicles", Iranian Journal of Electrical and Computer Engineering, Vol. 4, no. 2, pp. 85 - 93, 2005.
 Wong,K.P.,Wong,S.Y.W., "Combined Genetic Algorithm/ Simulated Annealing /Fuzzy Set to Short Term Generation Scheduling with Takeor Pay Fuel Contract", IEEE Trans. Power Systems, Vol.11, No.1, pp. 128-136, 1996.
 Wong,K.P.,Wong,S.Y.W., "Hybrid Genetic/Simulated Annealing to Short Term Multiple Fuel-Constrained Generation Scheduling", IEEE Trans. Power Systems, Vol.12, No.2, pp. 776-784, 1997.
 Mat Syai-in, Adi Soeprijanto, T. Hiyama.,"Generator Capability Curve Constraint for PSO based Optimal Power Flow". International Journal of Electrical power and Energy Systems Engineering Volume 3.2 .2010 pp 61-66.
 Jong Bae Park, etc, An Improved PSO for Economic Dispatch with Valve-Point Effect, Int, Journal of Innovations in Energy Ssystems and Power, Vol.1 no.1, Nov. 2006.
 Kennedy, J.; Eberhart, R "Particle swarm optimization " Proceedings., IEEE International Conference on Neural Networks, Vol4 Page(s): 1942 - 1948 1995.
 Gastaldo, P.; Zunino, R.; Vicario, E.; Heynderickx, I" CBP neural network for objective assessment of image quality " Proceedings of the International Joint Conference on Neural Networks,Vol 1 Page(s):194 -199 2003.