A hybrid adaptive teaching–learning-based optimization and differential evolution for parameter identification of photovoltaic models

Abstract

Photovoltaic (PV) systems play an important role in today’s power systems because they can convert solar energy directly into electricity. However, theirs conversion performance depends mainly on the PV models unknown parameters. Due to the complex characteristics of the equivalent circuit equation of the PV model, parameter identification of PV models remains a very popular and challenging task in PV system optimization. In this paper, a hybrid adaptive teaching–learning-based optimization (TLBO) with differential evolution (DE), referred to as ATLDE, is proposed to accurately and reliably identify the unknown parameters of PV models. In ATLDE, three improvements are introduced: i) the learners’ ranking probability is presented to adaptively choose the teacher or learner phase of TLBO; ii) based on the learners’ ranking probability, an enhanced teaching manner with an adaptive teaching factor TF is proposed to make use of the exploitation abilities of better learners in the teacher phase; iii) DE is embedded in the learner phase to improve population diversity and encourage wider exploration of the search space. In order to verify the performance of ATLDE, it is applied to parameter identification of different PV models, including the single diode model, the double diode model, and two PV panel module models. The experimental results demonstrate that our approach has great competitiveness in terms of accuracy and reliability. Therefore, the proposed algorithm can be an effective and efficient alternative for PV model parameter identification problems.