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

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 36 - Number 7
Year of Publication: 2011
Authors:
Mat Syai’in
Adi Soeprijanto
Eko Mulyanto Yuniarno
10.5120/4500-6351

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

@article{key:article,
	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}
}

Abstract

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.

References

  • Roa-Sepulveda, C.A. and B.J. Pavez-Lazo. “A solution to the optimal power flow using simulated annealing”. in Power Tech Proceedings, 2001 IEEE Porto. 2001.
  • B. Venkatesh, M. K. George, and H. B. Gooi, "Fuzzy OPF incorporating UPFC," IEE Proceedings-Generation, Transmission and Distribution, 2004. 151(5): p. 625-629.
  • Mori, H. and T. Horiguchi, "A genetic algorithm based approach to economic load dispatching," ANNPS '93.
  • Yurong, W., L. Fangxing, and W. Qiulan. "Reactive power planning based on fuzzy clustering and multivariate linear regression," 2010 IEEE Power and Energy Society General Meeting.
  • M. A. Abido, "Multiobjective particle swarm optimization for optimal power flow problem," MEPCON 2008.
  • L. dos Santos Coelho and V. C. Mariani. "Economic dispatch optimization using hybrid chaotic particle swarm optimizer," IEEE International Conference on Systems, Man and Cybernetics, 2007.
  • P. Jong-Bae, et al., "An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems," IEEE Transactions on Power Systems, 2010, 25(1): p. 156-166.
  • L. Weibing, L. Min, and W. Xianjia. "An improved particle swarm optimization algorithm for optimal power flow," IPEMC '09.
  • R. R. B. Aquino, et al., "Recurrent neural networks solving a real large scale mid-term scheduling for power plants," The 2010 International Joint Conference on Neural Networks (IJCNN)
  • K. K. Swarnkar, S. Wadhwani, and A. K. Wadhwani, "Optimal Power Flow of large distribution system solution for Combined Economic Emission Dispatch Problem using Partical Swarm Optimization. in Power Systems," ICPS '09
  • S. Panta, and S. Premrudeepreechacharn, "Economic dispatch for power generation using artificial neural network," ICPE '07.
  • G. Zwe-Lee, "Particle swarm optimization to solving the economic dispatch considering the generator constraints," IEEE Transactions on Power Systems, 2003. 18(3): p. 1187-1195.
  • P. E. Onate Yumbla, J. M. Ramirez, and C.A. Coello Coello, "Optimal Power Flow Subject to Security Constraints Solved With a Particle Swarm Optimizer," IEEE Transactions on Power Systems, 2008. 23(1): p. 33-40.
  • P. E. Sutherland, "Generator capability study for offshore oil platform," IEEE Industrial & Commercial Power Systems Technical Conference , 2009.
  • Li, W. and Z. Xia., "Online monitoring and fault diagnosis system of Power Transformer," APPEEC 2010.
  • N. Gunaseeli, and N. Karthikeyan. "A Constructive Approach of Modified Standard Backpropagation Algorithm with Optimum Initialization for Feedforward Neural Networks," International Conference on Computational Intelligence and Multimedia Applications, 2007.
  • L. Wenyuan, et al., "A Statistic-Fuzzy Technique for Clustering Load Curves," IEEE Transactions on Power Systems, 2007. 22(2): p. 890-891.
  • F.O. Karray,et al., Soft Computing and Intelligent Systems Design Pearson Education Limited 2004.
  • Mat Syai'in , Adi Soeprijanto,, and Takashi Hiyama,, Generator Capability Curve Constraint for PSO Based Optimal Power Flow; International Journal of Electrical and Electronic Engineering, 2010. 4(6): p. 371-376.