Precise and fast parameter identification of mono-crystalline, poly-crystalline, and mono-facial photovoltaic modules using a new Bat Artificial Bee Colony optimizer

Precise and fast identification of the photovoltaic model parameters plays a crucial role in many model-based design issues. It is the cornerstone of PV system simulation, control, and fault diagnosis. In this paper, a new optimizer is proposed to deal successfully with the difficulties of the PV modeling with considerable accuracy, reliability, and minimum execution time, especially for online applications. This new optimizer is named “Bat Artificial Bee Colony Optimizer” (BABCO), boosted with evolutionary strategies. It provides an accurate scheme to identify the unknown parameters of the different photovoltaic models based on the experimental current–voltage data. The performance of this optimizer was evaluated and compared to many recently published techniques and is tested by solving four problems of parameter estimation: PV cell model, mono-crystalline, poly-crystalline, and mono-facial photovoltaic modules model. The four problems were solved utilizing one diode model and double diode model-based circuit, and using, as the fitness function, the root mean square error (RMSE) with the employment of the Lambert W function to calculate the output current. The results and the performance metrics demonstrate the advantage of the proposed optimizer over several most cited methods in the literature.