MPPT of Solar PV Panels using Chaos PSO Algorithm under Varying Atmospheric Conditions
International Journal of Engineering Trends and Technology (IJETT) | ||
© 2014 by IJETT Journal | ||
Volume-15 Number-8 |
||
Year of Publication : 2014 | ||
Authors : Duy C. Huynh |
||
10.14445/22315381/IJETT-V15P274 |
Citation
Duy C. Huynh. "MPPT of Solar PV Panels using Chaos PSO Algorithm under Varying Atmospheric Conditions", International Journal of Engineering Trends and Technology (IJETT), V15(8),383-388 Sep 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
This paper proposes a novel application of a chaos particle swarm optimization (PSO) algorithm for tracking a maximum power point (MPP) of a solar photovoltaic (PV) panel under varying atmospheric conditions. Solar PV cells have a non-linear V-I characteristic with a distinct MPP which depends on environmental factors such as temperature and irradiation. In order to continuously harvest maximum power from the solar PV panel, it always has to be operated at its MPP. The proposed chaos PSO algorithm is one of the standard PSO algorithm variants. A chaos PSO algorithm with a logistic map has been used for initializing random values of MPPs, as well as the inertia weight in the velocity update equation of the standard PSO algorithm. This creates the best balance for the inertia weight during the evolution process of the standard PSO algorithm which results in the best convergence capability and search performance. Additionally, the algorithm has also been improved with regards to the diversity in the solution space through two independent chaotic random sequences. The obtained simulation results are compared with MPPs achieved using other algorithms such as the standard PSO, and Perturbation and Observation (P&O) algorithms. The results show that the chaos PSO algorithm is better than the standard PSO and P&O algorithms for tracking MPPs of solar PV panels.
References
[1] G. M. Master, Renewable and efficient electric power systems, A John Wiley & Sons, Inc., Publication, pp. 385-604, 2004.
[2] R. Faranda and S. Leva, “Energy comparison of MPPT techniques for PV systems”, WSEAS Trans. Power Syst., vol. 3, iss. 6, pp. 446-455, 2008.
[3] R. Sridhar, S. Jeevananthan, N. T. Selvan and P. V. Sujith Chowdary, “Performance improvement of a photovoltaic array using MPPT P&O technique”, IEEE Int. Conf. Commun. Control and Comput. Technol., ICCCCT 2010, pp. 191-195, 2010.
[4] N. M. Razali and N. A. Rahim, “DSP-based maximum peak power tracker using P&O algorithm”, IEEE First Conf. Clean Energy and Technol., CET 2011, pp. 34-39, 2011.
[5] B. Liu, S. Duan, F. Liu and P. Xu, “Analysis and improvement of maximum power point tracking algorithm based on incremental conductance method for photovoltaic array”, 7th Int. Conf. Power Electron. and Drive Syst., PEDS 2007, pp. 637-641, 2007.
[6] W. Ping, D. Hui, D. Changyu and Q. Shengbiao, “An improved MPPT algorithm based on traditional incremental conductance method”, 4th Int. Conf. Power Electron. Syst. and Applicat., PESA 2011, pp. 1-4, 2011.
[7] Y. Zhihao and W. Xiaobo, “Compensation loop design of a photovoltaic system based on constant voltage MPPT”, Asia-Pacific Power and Energy Eng. Conf., APPEEC 2009, pp. 1-4, 2009.
[8] K. A. Aganah and A. W. Leedy, “A constant voltage maximum power point tracking method for solar powered systems”, IEEE 43rd Southeastern Sym. Syst. Theory, SSST 2011, pp. 125-130, 2011.
[9] R. Ramaprabha, V. Gothandaraman, K. Kanimozhi, R. Divya and B. L. Mathur, “Maximum power point tracking using GA-optimized artificial neural network for solar PV system”, 1st Int. Conf. Elect. Energy Syst., ICEES 2011, pp. 264-268, 2011.
[10] S. J. Kang, J. S. Ko, J. S. Choi, M. G. Jang, J. H. Mun, J. G. Lee and D. H. Chung, “A novel MPPT control of photovoltaic system using FLC algorithm”, 11th Int. Conf. Control, Automat. and Syst., ICCAS 2011, pp. 434-439. 2011.
[11] V. Padmanabhan, V. Beena and M. Jayaraju, “Fuzzy logic based maximum power point tracker for a photovoltaic system”, Int. Conf. Power, Signals, Controls and Comput., EPSCICON 2012, pp. 1-6, 2012.
[12] Md. A. Azam, S. A. A. Nahid, M. M. Alam and B. A. Plabon, “Microcontroller based high precision PSO algorithm for maximum solar power tracking”, Int. Conf. Inform., Electron. and Vision, ICIEV 2012, pp. 292-297, 2012.
[13] K. Ishaque, Z. Salam, M. Amjad and S. Mekhilef, “An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillation”, IEEE Trans. Power Electron., pp. 3627-3638, 2012.
[14] J. Kennedy and R. Eberhart, “Particle swarm optimization”, Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942-1948, 1995.
[15] F. V. D. Bergh, An analysis of particle swarm optimizers, Ph.D. dissertation, Dept. Comput. Sci., Pretoria Univ., Pretoria, South Africa, 2001.
[16] A. Ratnaweera, S. K. Halgamuge and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients”, IEEE Trans. Evol. Comput., vol. 8, pp. 240-255, 2004.
[17] Y. Shi and R. Eberhart, “A modified particle swarm optimizer”, Proc. IEEE Int. Conf. Evol. Computation, Piscataway, New Jersey, pp. 69-73, 1998.
[18] B. Alatas, E. Akin and A. B. Ozer, “Chaos embedded particle swarm optimization algorithms”, J. Chaos, Solitons & Fractals, vol. 40, issue 4, pp. 1715-1734, 2009.
[19] B. Liu, L. Wang, Y. H. Jin, F. Tang and D. X. Huang, “Improved particle swarm optimization combined with chaos”, J. Chaos, Solitons & Fractals, vol. 25, issue 5, pp. 1261-1271, 2005.
[20] H. J. Meng, P. Zheng, R. Y. Wu, X. J. Hao and Z. Xie, “A hybrid particle swarm algorithm with embedded chaotic search”, Proc. 2004 IEEE Conf. Cybern. and Intelligent Syst., Singapore, pp. 367-371, 2004.
[21] Y. Feng, G. F. Teng, A. X. Wang and Y. M. Yao, “Chaotic inertia weight in particle swarm optimization”, 2nd Int. Conf. Innovative Computing, Inform. and Control, ICICIC ’07, pp. 475-478, 2007.
[22] Y. Feng, Y. M. Yao and A. X. Wang, “Comparing with chaotic inertia weights in particle swarm optimization”, Proc. 6th Int. Conf. Mach. Learning and Cybern., Hong Kong, pp. 329-333, 2007.
[23] D. C. Huynh and M. W. Dunnigan, “Parameter estimation of an induction machine using advanced particle swarm optimization algorithms”, IET J. Elect. Power Applicat., vol. 4, no. 9, pp. 748-760, 2010.
[24] D. Sera, R. Teodorescu and P. Rodriguez, “PV panel model based on datasheet values”, IEEE Int. Sym. Ind. Electron., ISIE 2007, pp. 2392-2396, 2007.
Keywords
Solar photovoltaic panels, Maximum power point tracking, and Particle swarm optimization algorithm.