Evaluation of constraint in photovoltaic cells using ensemble multi-strategy shuffled frog leading algorithms
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The efficient identification of the unknown and changeable photovoltaic parameters is a matter of considerable interest to model photovoltaic systems. The accurate and efficient parameters are important in converting the entire photovoltaic system from solar to electricity. This paper, an ensemble multi strategy-driven shuffled frog leading algorithm (EMSFLA), is proposed to optimize photovoltaic modules' parameters and enhance solar energy conversion efficiency. In the EMSFLA, opposition-based learning can consider the opposite position in each frog memeplex to enhance the convergence velocity and keep the population diversity. The mutation and crossover operators abstracted from differential evolution with greedy strategy can better balance diversification and intensification during the optimization process. Then, the performance of the EMSFLA is preliminarily verified on representative benchmark functions compared to a slice of state-of-the-art algorithms. After that, the EMSFLA is employed to identify these parameters of single, double diode effectively, and photovoltaic modules thoroughly. Finally, the proposal's stability is further investigated on various temperatures and irradiation hierarchies on several manufacturers' datasheets. The outcome of statistical experiments has indicated that the EMSFLA performs higher accuracy and reliability in estimating photovoltaic mode's critical parameters, and it may be taken as a potential tool for parameter identification tasks in photovoltaic systems. For further info about this research, you can visit resources at https://aliasgharheidari.com.
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