Optimal equivalent circuit models for photovoltaic cells and modules using multi-source guided teaching–learning-based optimization

Abstract

The complexity of equivalent circuit models of photovoltaic cells and modules poses a difficult task to the parameter extraction methods. Teaching-learning-based optimization (TLBO) is a potent metaheuristic-based parameter extraction method, but it suffers from insufficient precision and low dependability. This study presented a multi-source guided TLBO through improving its two optimization phases. A multi-source guided approach with one-to-one and step-by-step teaching strategies was designed to guide different learners in the teacher phase. Besides, different strategies based on multiple learners were introduced for learners with different knowledge reserves to strengthen information exchanging. With the improvements, it is advantageous to lessen the likelihood of hitting a local optimum and thereby the global convergence can be accelerated. The resultant method was verified on single diode model, double diode model, and three additional modules. The findings demonstrate that it obtained better solutions in precision and dependability, and stood out from the crowd of algorithms.

Keywords

• Metaheuristic algorithm
• Parameter extraction
• Photovoltaic cell
• Teaching–learning-based optimization