Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm
International Journal of Energy and Power Engineering
Volume 6, Issue 6, December 2017, Pages: 91-99
Received: Oct. 10, 2017; Accepted: Oct. 23, 2017; Published: Dec. 7, 2017
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Said Zakaria Said, Department of Electrical Engineering, Pan African University for Basic Sciences Technology and Innovation, Nairobi, Kenya; Department of Mechanical Engineering, University Polytechnic Institute of Mongo, Ndjamena, Chad
Lamine Thiaw, Department of Electrical and Information Engineering, University of Nairobi, Nairobi, Kenya
Cyrus Wekesa Wabuge, Department of Electrical Engineering, Ecole Superieure Polytechnique, Chekh Anta Diop University, Dakar, Senegal
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This paper addresses the research methodology for Maximum Power Point Tracking (MPPT). Photovoltaic (PV) Generators may receive different level of solar irradiance and temperature, such as partially shaded by clouds, tree leaves or nearby building. Under partial shaded conditions, several peak power points can occur when the PV module is shaded, which would significantly reduce the energy produced by PV Generators without proper control. Therefore, a Maximum Power Point Tracking (MPPT) Algorithm is used to extract the maximum available PV power from the PV array. However, the common used conventional MPPT algorithms are unable to detect global peak (GP) power point with the presence of several local peaks (LP). In this paper, a hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed to detect the global peak power. MATLAB/Simulink is used to simulate a PV system which consists of PV Generators, DC–DC boost converter, a hybrid PSO-ANN Algorithm, and a resistive load. The simulation results are compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array even under partial shaded conditions.
Maximum Power Point Tracking, Particle Swarm Optimization, Artificial Neural Network, Photovoltaic Generators
To cite this article
Said Zakaria Said, Lamine Thiaw, Cyrus Wekesa Wabuge, Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm, International Journal of Energy and Power Engineering. Vol. 6, No. 6, 2017, pp. 91-99. doi: 10.11648/j.ijepe.20170606.12
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Brito MAG, Galotto L Jr, Sampaio LP, Melo GA, Canesin CA (2013) Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Trans Industr Electron 60 (3):1156–1167.
Thiaw, L., Sow, G., & Fall, S. (2014). Application of Neural Networks Technique in Renewable Energy Systems.
M. G. Villalva, J. R. Gazoli and E. Ruppert F. “Analysis and simulation of the P&O MPPT algorithm using a linearized array model”. Power electronics conference, 2009, Brazil. pp 189 – 195.
Xu W, Mu C, Jin J (2014) Novel linear iteration maximum power point tracking algorithm for photovoltaic power generation. IEEE Trans Appl Supercond 24 (5):1–6.
Ji YH, Jung DY, Kim JG, Kim JH, Lee TW, Won CY (2011) A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions. IEEE Trans Power Electron 26 (4):1001–1009.
Blanes JM, Toledo FJ, Montero S, Garrigós A (2013) In-site real-time photovoltaic I-V curves and maximum power point estimator. IEEE Trans Power Electron 28 (3):1234–1240.
A. K. Rai, N. D. Kaushika, B. Singh, N. Agarwal, Simulation model of ANN based maximum power point tracking controller for solar PV system, Solar Energy Materials and Solar Cells, 95 (2), pp. 773778, 2011.
A. A. Kulaksız, ve R. Akkaya, Training Data Optimization for ANNs using Genetic Algorithms to Enhance MPPT Efficiency of a Stand-alone PV System, Turkish Journal of Electrical Engineering & Computer Sciences, 20 (2), pp. 241-254, 2012.
Hartmann LV, Vitorino MA, Correa MBR, Lima AMN (2013) Combining model-based and heuristic techniques for fast tracking the maximum-power point of photovoltaic systems. IEEE Trans Power Electron 28 (6):2875–2885.
M. Miyatake, M. veerachary, F. Toriumi, N. Fujii, and H. Ko, “Maximum power point tracking of multiple photovoltaic arrays: a PSO approach,” IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 367–380, Jan. 2011.
K. L. Lian, J. H. Jhang, and I. S. Tian, “A maximum power point tracking method based on perturb-and-observe combined with particle swarm optimization,” IEEE J. Photovoltaics, vol. 4, no. 2, pp. 626–633, Mar. 2014.
R. B. A. Koad and A. F. Zobaa, “Comparison between the conventional methods and PSO based MPPT algorithm for photovoltaic systems,” World Academy of Science, Engineering and Technology, International Science Index 88, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, vol. 8, no. 4, pp. 673-678. 2014.
Punitha K.; Devaraj D.; Sakthivel S., Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions, In: Energy Vol. 62, December 2013, Page (s): 330-340.
M. Abdulkadir, A. S. Samosir, and A. H. M. Yatim, “Modelling and simulation of maximum power point tracking of photovoltaic system in Simulink model,” in Proceedings of the IEEE International Conference on Power and Energy (PECon’12), pp. 325–330, Kota Kinabalu, Malaysia, December 2012.
M. Miyatake, F. Toriumi, T. Endo, and N. Fujii, “A novel maximum power point tracker controlling several converters connected to photovoltaic arrays with particle swarm optimization technique,” in Proceedings of the European Conference on Power Electronics and Applications (EPE ’07), pp. 1–10, Aalborg, Denmark, September 2007.
Lyden S, Haque ME (2015) Maximum Power Point Tracking techniques for photovoltaic systems: a comprehensive review and comparative analysis. Renew Sustain Energy Rev 52:1504–1518.
R. Ramaprabha and B. L. Mathur, “Genetic Algorithm based Maximum Power Point Tracking for Partially Shaded Solar Photovoltaic Array,” International Journal of Research and Reviews in Information Sciences, vol. 2, no. 1, 2012, pp. 161-163.
K. T. K. Teo, P. Y. Lim, B. L. Chua, H. H. Goh, and M. K. Tan, “Maximum power point tracking of partially shaded photovoltaic arrays using particle swarm optimization,” Proc. 4 th International Conference on Artificial Intelligence with Applications in Engineering and Technology, Kota Kinabalu, Malaysia, 2014, pp. 247-252, doi: 10.1109/ICAIET.2014.48
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