Premkumar, M., Kumar, C., Sowmya, R.: Mathematical modelling of solar photovoltaic cell/panel/array based on the physical parameters from the manufacturer’s datasheet. Rasheed, M.S., Shihab, S.: Modelling and parameter extraction of PV cell using single-diode model. Īyang, A., et al.: Maximum likelihood parameters estimation of single-diode model of photovoltaic generator. Premkumar, M., Sowmya, R., Umashankar, S., Jangir, P.: Extraction of uncertain parameters of single-diode photovoltaic module using hybrid particle swarm optimization and grey wolf optimization algorithm. Īgwa, A.M., El-Fergany, A.A., Maksoud, H.A.: Electrical characterization of photovoltaic modules using farmland fertility optimizer. Zhou, J., Yu, Z., Lu, Z., Li, C., and Zhang, R.: “Study of Photovoltaic Cells Engineering Mathematical Model,” in IOP Conference Series: Materials Science and Engineering, 2016, vol. Moshksar, E., Ghanbari, T.: Adaptive estimation approach for parameter identification of photovoltaic modules. Premkumar, M., Kumar, C., Sowmya, R., Pradeep, J.: A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems. Premkumar, M., Sumithira, R.: Humpback whale assisted hybrid maximum power point tracking algorithm for partially shaded solar photovoltaic systems. Premkumar, M., Sowmya, R.: An effective maximum power point tracker for partially shaded solar photovoltaic systems. Hussin, F., Issabayeva, G., Aroua, M.K.: Solar photovoltaic applications: opportunities and challenges. Green, M.A., Bremner, S.P.: Energy conversion approaches and materials for high-efficiency photovoltaics. With the average Friedman’s ranking test value of 1.6666 and the average runtime of 17.08, the RCOA stands first among all selected algorithms. It could be used as a viable approach for parameter identification problems in PV systems. Also, the results have revealed that the RCOA has superior reliability and accuracy when estimating the three-diode PV model parameters. The proposed methodology was found to be a trustworthy tool and it is proved through a statistical analysis and non-parametric test. The performance of the RCOA is compared with state-of-the-art algorithms, and the obtained results prove the superiority of the RCOA. The proposed algorithm combines an improved version of the Newton–Raphson method to find the optimal PV parameters in fewer iterations. The validity of the proposed RCOA is tested on the solar PV cell/module equivalent circuit parameter estimation of the three-diode PV model. It is proposed that the RCOA be used as a single-objective algorithm that is simple, reliable, and has zero sensitive parameters, thereby being a parameter-free algorithm. Both of these stages of the RC circuit are essential to model the algorithm properly. The RC circuit's total time response can be divided into steady-state and transient-state. Therefore, we propose and apply for the first time a unique physics-based metaheuristic algorithm known as the Resistance–Capacitance Optimization Algorithm (RCOA), which is based on the concept of resistance–capacitance circuit response, and it was obviously inspired by the output response of a Resistor–Capacitor (RC) circuit when the input is connected or disconnected suddenly. It is critical to have effective and accurate parameters when transforming the complete PV system from solar energy to electrical energy. Identifying and estimating uncertain and dynamic photovoltaic characteristics with high accuracy is important when modeling solar photovoltaic (PV) systems.
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