Volume 6, Issue 6, December 2017, Page: 91-99
Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm
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
Received: Oct. 10, 2017;       Accepted: Oct. 23, 2017;       Published: Dec. 7, 2017
DOI: 10.11648/j.ijepe.20170606.12      View  2307      Downloads  146
Abstract
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.
Keywords
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
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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