Enhanced Parameter Estimation Approach for Modeling of Three-Diode Solar Photovoltaic System

Parameter estimation of the photovoltaic (PV) models is required to be accurate to attain high efficacy and reliability of solar energy systems. This paper introduces a more advanced model construction and optimization method of PV parameter estimation with three-diode model using Teaching-Learning-Based Optimization (TLBO) optimization algorithm. The three-diode model (TDM) has the benefit of being able to model both complex recombination and leakage processes in the solar cell with better accuracy under different irradiance and temperature conditions than single or two-diode models. TLBO was inspired by the teaching-learning process of classroom and was proposed to estimate the model parameters. The process is designed to reduce the root mean square error RMSE between the measured and simulated I-V data of current voltage. The proposed TLBO-based algorithm is implemented on the RTC France solar cell data and is found to be superior in convergence and accuracy over the algorithms used as benchmarking algorithms. The results confirm that TLBO is an efficient and reliable tool in the PV system modeling and optimization and can be applied to find a balance between global exploration and local exploitation without any algorithm-specific control parameters.