An Intelligent Tunicate Swarm Algorithm Based MPPT Control Strategy for Multiple Configurations of PV Systems Under Partial Shading Conditions. Issue 12 (25th October 2021)

• Record Type: Journal Article
• Authors: Mansoor, Majad; Mirza, Adeel Feroz; Long, Fei; Ling, Qiang
• Is Part Of: Advanced theory and simulations. Volume 4:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-25
• Subjects: global maxim (GM) -- improved particle swarm optimization (IPSO) -- maximum power point tracking (MPPT) -- photovoltaics (PV) -- swarm intelligence (SI) -- tunicate swarm algorithm (TSA); Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202100246
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Solar energy is the most techno‐economically viable renewable source of energy and can be effectively converted into electrical power by Photovoltaic (PV) systems. As partial shading (PS) may reduce the harvested energy of PV systems, this paper proposes a novel tunicate swarm algorithm (TSA) based MPPT (maximum power point tracking) strategy to tackle the PS issue. More specifically, the simple and effective modeling of TSA with a search and skipping (SAS) scheme is utilized to minimize the tracking time and search area. The SAS scheme can discard the voltage range lacking global maximum power point (GMPP) and significantly reduce computation time. Consequently, power tracking, tracking time, and robustness can be greatly enhanced. The performance of the proposed TSA strategy is comprehensively analyzed against state‐of‐the‐art techniques, including incremental conductance (InC), improved particle swarm optimization (IPSO), grey wolf optimization (GWO), and cuckoo search algorithm (CSA), through detailed case studies, which include standard array configurations, PS conditions, varying irradiance patterns, fast‐changing temperature, and the field atmospheric data. TSA is further validated on a low‐cost hardware setup, confirming its superior performance. The results provide insightful validation of the practicality of the proposed TSA strategy in the real‐world applications. Abstract : It proposes a novel implementation of TSA for MPPT of PV systems under PSC and fast varying temperature conditions. TSA exploration and exploitation are balanced with accelerated convergence toward the best solution. Distance between source and destination influences social behavior. Effective information sharing enables successful GMPP capability for PV systems PS problems. Experimental results confirm the superiority of TSA.