Vol.68, No.1, 2021, pp.29-43, doi:10.32604/cmc.2021.014824
OPEN ACCESS
ARTICLE
A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays
  • Kamran Sadiq Awan1, Tahir Mahmood1, Mohammad Shorfuzzaman2, Rashid Ali3, Raja Majid Mehmood4,*
1 Department of Electrical Engineering, University of Engineering and Technology, Taxila, 47080, Pakistan
2 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 School of Intelligent Mechatronics Engineering, Sejong University, Korea
4 Department of Information and Communication Technology, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang, 43900, Malaysia
* Corresponding Author: Raja Majid Mehmood. Email:
(This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
Received 20 October 2020; Accepted 26 November 2020; Issue published 22 March 2021
Abstract
Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions, there is zero or no partial shading. But the changing irradiance causes a shading effect which is called Partial Shading. Many conventional and soft computing techniques have been in use to harvest solar energy. These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong. In this paper, a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning (OBL) to deal with partial shading conditions. Simulation studies on different cases of partial shading have proven this technique effective in attaining MPP.
Keywords
Maximum power point tracking; flower pollination algorithm; opposition-based-learning; flower pollination algorithm hybridized with opposition based learning
Cite This Article
K. S. Awan, T. Mahmood, M. Shorfuzzaman, R. Ali and R. M. Mehmood, "A machine learning based algorithm to process partial shading effects in pv arrays," Computers, Materials & Continua, vol. 68, no.1, pp. 29–43, 2021.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.