Self-adaptive Teaching-Learning-Based Optimization with Reusing Successful Learning Experience for Parameter Extraction in Photovoltaic Models

This paper proposes a self-adaptive teaching-learning-based optimization with reusing successful learning experience (RSTLBO) to accurately and reliably extract parameters of different photovoltaic (PV) models. The key novelties of RSTLBO are: 1) Learners adaptively choose teacher or learner phase based on a selection probability according to their performance, balancing exploration and exploitation; 2) Successful learner experiences are reused to enhance search capability. Experiments on single diode, double diode and PV panel models demonstrate that RSTLBO achieves higher accuracy and faster convergence than state-of-the-art methods like P-DE, TLBO, GOTLBO, etc. Specifically, RSTLBO obtains the minimum RMSE across all models, outperforms compared methods in statistical results, and exhibits fastest convergence in almost all cases. The self-adaptive probability selection and experience reuse make RSTLBO effective for PV parameter extraction.