Summary

Proceedings of the 2013 International Symposium on Nonlinear Theory and its Applications

2013

Session Number:B1L-B

Session:

Number:197

Insensitive Differential Evolution for Exploring Maximum Power Point

Naoto Ando,  Masaya Muraoka,  Toshimichi Saito,  

pp.197-200

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.2.197

PDF download (2.1MB)

This paper studies an insensitive differential evolution and its application to exploring the maximum power point in photovoltaic systems. Depending on the insolation, the maximum power point varies and complicated multi-peak is generated. Our algorithm has a key parameter that controls particles insensitivity that can be effective to prevent trapping of particles. Performing basic numerical experiments, the algorithm efficiency is investigated.

[1] A. P. Engelbrecht, Fundamentals of computational swarm intelligence, Willey, 2005.

[2] R. Storn and K. Price, ”Differrential evolution - a simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimaization, Vol. 11, pp. 341-359, 1997.

[3] H. Lampinen, and O. Vainio, ”An optimization approach to designing OTAs for low-voltage sigmadelta modulators”, Proc. of the 2001 WCCI, pp. 1665-1671, 2001.

[4] B. Luitel and G. K. Venayagamoorthy, ”Differential evolution particle swarm optimization for digital filter design”, Proc. of the 2008 IEEE, pp. 3954-396, 2008.

[5] M. Miyatake, M. Veerachary, F. Toriumi, N. Fujii and H. Ko, ”Maximum Power Point Tracking of Multiple Photovoltaic Arrays: A Particle Swarm Optimization Approach” IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, No. 1, pp. 367-380, 2011.

[6] T. L. Nguyen and K.-S. Low, ”A Global Maximum Power Point Tracking Scheme Employing DIRECT Search Algorithm for Photovoltaic Systems”, IEEE Trans. Industrial, Vol. 57, No.10, pp. 3456-3467, 2010.

[7] G. Vachtsevanos and K. Kalaitzakis, ”A Hybrid Photovoltaic Simulator for Utility Interactive Studies”, IEEE Transactions on Energy Conversion, Vol. EC-2, No. 2, pp. 227-231, 1987.

[8] M. Muraoka, N. Mikami and T. Saito, Exploring Maximum Power Point by Population-Based Optimization Algorithms, Proc. NOLTA, pp. 618-621, 2012