Parameter estimation of single, double, and three-diode photovoltaic cell models using an advanced optimization algorithm

With the rising worldwide need for green power sources, dependable and efficient photovoltaic (PV) systems are emerging as one of the most promising choices among sustainable energy supplies. Precise modeling of solar generators is crucial to improve their energy conversion efficiency, rendering this necessity even more critical. The precise extraction of photovoltaic (PV) model parameters is crucial for the assessment of the PV system. Many current techniques for obtaining photovoltaic parameters exhibit poor precision and are susceptible to local optima. A Circulatory System Based Optimization (CSBO) is developed to address these flaws. This research presents an innovative approach for extracting essential characteristics of the single diode model (SDM), double diode model (DDM), and three diode model (TDM) of RTC FRANCE silicon solar cells utilizing the recently discovered CSBO, which has not been previously employed in this context. A CSBO utilizes the root mean square error (RMSE) to precisely compute the current of a PV unit and is examined. Non-linear equations are employed for the accurate resolution of the I-V curve in RTC FRANCE silicon solar cells. Comprehensive simulations are performed to assess the efficacy of the suggested strategy and its efficacy relative to other metaheuristic techniques. The findings indicate that CSBO markedly decreases the Root Mean Square Error (RMSE), achieving optimal RMSE values of 9.860218 × 10 − 04 , 9.824848 × 10 − 04 ,9.803370 × 10 − 04 , and 2.425074 × 10 − 03 for SDM, DDM, TDM, and Photowatt-PWP-201 module respectively, with a smaller RT (Running Time) for all the PV cell models. The findings validate the enhanced performance and precision of CSBO which is adopts circulation-inspired search strategies to enhance the global exploration, local exploitation, population diversity, convergence speed, and premature convergence avoidance, relative to current methodologies, demonstrating that it yields more dependable parameter estimations and contributes to increased energy conversion efficiency in solar systems.