Generalized normal distribution optimization and its applications in parameter extraction of photovoltaic models

Abstract The accuracy of extracting the unknown parameters of photovoltaic models is closely related with the effectiveness of modeling, simulating, and controlling photovoltaic systems. Metaheuristics have been widely used for improving the accuracy of extracting the unknown parameters of photovoltaic models. Despite the success of such techniques in this application area, they require parameter adjustment, which will restrict their applications especially for non-expert users. This is the motivation of this work, in which a novel metaheuristic is proposed called generalized normal distribution optimization, the proposed method is inspired by the generalized normal distribution model; each individual uses a generalized normal distribution curve to update its position. Unlike the majority of metaheuristics, the proposed method only needs the essential population size and terminal condition to solve optimization problems. In order to benchmark the performance of the proposed method, it is employed to extract the unknown parameters of three photovoltaic models including single diode model, double diode model and photovoltaic module model. The solutions obtained by the proposed method are compared with those of ten state-of-the-art metaheuristic algorithms and some recent reported solutions. Experimental results demonstrate the excellent performance of the proposed method for parameter extraction of the applied photovoltaic models in terms of quality and stable of the obtained solutions. 1

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