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New Algorithm for Neural Network Optimal Power Flow (NN-OPF) including Generator Capability Curve Constraint and Statistic-Fuzzy Load Clustering

International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 36 - Number 7
Year of Publication: 2011
Mat Syai’in
Adi Soeprijanto
Eko Mulyanto Yuniarno

Mat Syai'in, Adi Soeprijanto and Eko Mulyanto Yuniarno. Article: New Algorithm for Neural Network Optimal Power Flow (NN-OPF) including Generator Capability Curve Constraint and Statistic-Fuzzy Load Clustering. International Journal of Computer Applications 36(7):1-8, December 2011. Full text available. BibTeX

	author = {Mat Syai'in and Adi Soeprijanto and Eko Mulyanto Yuniarno},
	title = {Article: New Algorithm for Neural Network Optimal Power Flow (NN-OPF) including Generator Capability Curve Constraint and Statistic-Fuzzy Load Clustering},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {36},
	number = {7},
	pages = {1-8},
	month = {December},
	note = {Full text available}


This paper presents a novel algorithm of an optimal power flow (OPF), which possible be used for real time applications. The proposed algorithm uses neural networks (NNs) to model the generator capability curves and set them as the output power constraints of the generators. In addition, it also uses NNs to replace an OPF based on the particle swarm optimization (PSO) method so as to run in real time. Also, in order for the proposed algorithm to be able to account for various load conditions, the statistic-fuzzy load clustering method is used to classify the loads based on the patterns of load curves. A similarity index is then defined to associate the similarity among different patterns of load distribution curves. This similarity index is also included in the training process of the final constructed neural networks. A 500 kV Java-Bali power system consisting of 23 buses is used as a benchmark system to validate the proposed NN-based OPF. The simulation results show that that the values obtained from the proposed algorithm are in great agreement with those calculated from the PSO-OPF.


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