Comparative analysis of two-step GA-based PV array reconfiguration technique and other reconfiguration techniques

• Published in: Energy conversion and management Vol. 230; p. 113806
• Main Authors: Muhammad Ajmal, Aidha, Ramachandaramurthy, Vigna K., Naderipour, Amirreza, Ekanayake, Janaka B.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.02.2021; Elsevier Science Ltd
• Subjects: administrative management; Algorithms; Array reconfiguration; Arrays; Comparative analysis; Comparative studies; comparative study; computer software; energy conversion; Genetic algorithm; Genetic algorithms; monitoring; Optimization; Panels; Particle swarm optimization; Photovoltaic; Photovoltaic cells; Photovoltaics; Reconfiguration; shade; Shading; solar collectors; Wiring; Array reconfiguration; Photovoltaic; Genetic algorithm; Optimization
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2020.113806

•Dynamic reconfiguration with GA for TCT PV array to disperse the shading effect.•The new technique obtains the optimal configuration and improve the generated power.•The proposed technique is applied for different sizes of PV array.•The proposed technique overcomes the issue of scaling to larger applications. 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.