Differential evolution with classified mutation for parameter extraction of photovoltaic models.

Abstract

Existing algorithms for photovoltaic (PV) parameter extraction struggle to balance accuracy and computational efficiency when handling complex models. To address this gap, a differential evolution with classified mutation (DECM) is proposed, which integrates adaptive mutation strategies and a hierarchical classification framework to improve both scalability and precision. In DECM, all individuals are divided into many subswarms. The best position in each sub-swarm is considered the locally best position. Two different mutation strategies are developed for these local best positions. For the other individual positions, a different mutation strategy is used to improve these positions. The DECM utilizes a multi-swarm approach to allocate specific roles to individual particles, followed by the implementation of role-specific mutation strategies. In contrast to some other differential evolution algorithms, the DECM eliminates both crossover operations and parameter tuning strategies, thereby offering enhanced simplicity and operational efficiency. To better understand the effectiveness of DECM, several photovoltaic models are adopted. According to the experimental results, DECM outperforms some popular algorithms in terms of solution accuracy, computational efficiency and parameter extraction robustness.