Abstract
In photovoltaic power plants, operating environmental conditions have a profound effect on the behavior and the quality of the photovoltaic (PV) energy produced. The non-homogenous insolation reduces the power producing capacity, introduces multiple peaks in the PV curve, and produces hot spots. Metaheuristic algorithms have emerged as an effective tool in reconfiguring the panels to disperse the shading uniformly. The actual locations of the modules remain unchanged during reconfiguration while the electrical connection is changed. Therefore, this paper presents a comprehensive study on two different metaheuristic optimization algorithms, namely particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). These optimization tools provide a connecting matrix for the new electrical connection that gives high output power to the same PV array. In addition, a comparison of these reconfiguration methods is done to assess their suitability of these methods. Based on the result, PSO gives better output as compared to GOA both in terms of power produced as well as the PV curve.
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Dhariwal, R., Kumar, B. (2023). A Comparative Study on Metaheuristic-Based Reconfiguration Strategies for Non-uniformly Shaded PV Array . In: Rani, A., Kumar, B., Shrivastava, V., Bansal, R.C. (eds) Signals, Machines and Automation. SIGMA 2022. Lecture Notes in Electrical Engineering, vol 1023. Springer, Singapore. https://doi.org/10.1007/978-981-99-0969-8_17
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DOI: https://doi.org/10.1007/978-981-99-0969-8_17
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