Photovoltaic parameter estimation using improved moth flame algorithms with local escape operators

Optimizing, regulating, and simulating photovoltaic systems are crucial for producing solar energy. The performance of PV systems is greatly affected by model parameters, which can be variable and not always easily accessible. As a result, finding these model parameters is a constant goal. Current-voltage data is needed to extract characteristics of solar modules and construct high-accuracy models for the modeling, control, and optimization of photovoltaic systems. This paper introduces an Improved Moth Flame algorithm with Local escape operators (IMFOL). The LEO technique improves the MFO algorithm's efficiency and the results' precision. Furthermore, the LEO mechanism enhances both the diversity of the population and the MFO's exploration efficiency. This keeps the exploration and exploitation rates in equilibrium. The IMFO achieved the lowest RMSE for a double diode module, 9.8252542E-04, while IMFOL and JADE had the best RMSE for a single diode module, 9.8602E-04. Additionally, IMFOL obtained 2,42521000E-03 for the Photowatt-PWP 201 model and 1,72981457E-03 for the STM6-40/36 model. The proposed method surpasses the state-of-the-art in terms of accuracy, reliability, and output. IMFOL is a reliable tool for evaluating solar cell and PV module data.