Publication: Comparative analysis of two-step GA-based PV array reconfiguration technique and other reconfiguration techniques
Date
2021
Authors
Muhammad Ajmal A.
Ramachandaramurthy V.K.
Naderipour A.
Ekanayake J.B.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
Photovoltaic (PV) plants can be exposed to partial shading, which reduces the energy production and causes multi-peaks to form in the Power-Voltage (P-V) curve. As a result, the row currents of the PV modules will not be constant. Several techniques have been proposed to overcome partial shading, such as the static and dynamic reconfiguration techniques, with both aiming to reduce the difference in the row currents to improve energy production. Minimization of the row current via static techniques requires laborious work and extra wiring. On the other hand, dynamic techniques require an extensive monitoring system to support different tasks. Therefore, to improve the power generated from the PV array, this paper suggests a new reconfiguration technique for PV panels using Genetic algorithm (GA) and two main reconfigurable steps based on a switching matrix. In this technique, only the electrical connections of the PV panels are changed while its physical location remains unchanged. To verify the effectiveness of the proposed reconfiguration technique, the system was simulated and tested using MATLAB/SIMULINK software, with four shading patterns. The results were compared with other reconfiguration techniques, namely TCT configuration, competence square (CS), SuDoKu, two-phase array reconfiguration, Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Modified Harris Hawks Optimization (MHHO). The performance of each shading case was also analyzed. Also, a comparative study on performance analysis in real-time application was carried out for each shading pattern. The results prove the superiority of the proposed technique over other techniques for overcoming partial shading. � 2020 Elsevier Ltd
Description
Dynamic models; Electric connectors; MATLAB; Particle swarm optimization (PSO); Photovoltaic cells; Software testing; Solar power generation; Comparative analysis; Comparative studies; Dynamic reconfiguration techniques; Dynamic techniques; Electrical connection; Matlab/Simulink software; Performance analysis; Real-time application; Genetic algorithms