Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization

Abstract

Accurate and reliable parameter extraction of photovoltaic (PV) models is urgently desired for the simulation, evaluation, control, and optimization of PV systems. Although many meta-heuristic algorithms have been used to extract the PV parameters, the extracted parameters are usually not very accurate and reliable. To accurately and reliably extract the parameters of different PV models, an improved teaching-learning-based optimization (ITLBO) algorithm is proposed in this paper. The novelty of ITLBO lies primarily in the improved teaching and learning strategies with two improvements: (i) the teacher adopts different teaching strategies according to learner levels in the teacher phase; and (ii) in the learner phase, a new learning strategy is proposed to balance exploration and exploitation. The performance of ITLBO is verified by extracting the parameters of the single diode model, the double diode model, and three PV modules. The experimental results indicate that ITLBO obtains better performance with respect to accuracy and reliability compared to the other algorithms.