Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning

The performance of photovoltaic (PV) cell is affected by the model structure and corresponding parameters. However, these parameters are adjustable and variable, which play an available role in regarding to the efficiency and effectiveness of PV generation. Due to strong non-linear characteristics, existing PV model parameters identification methods cannot easily obtain accurate solutions. To tackle this, this paper proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOL), named DOLADE. In DOLADE, the opposite learning method expands the current elite population and the population of poor performance, improving the particles' exploration capability. In the process of particles work, the searching area of particles is adjusting dynamically so that the particles' exploitation capability is enhanced. The experimental data of different types of PV are tested, respectively. Three PV models are used to verify the new strategy's accuracy and effectiveness. The proposed DOLADE is compared with several general advanced algorithms, and comprehensive experimental results are demonstrated. The results illustrate that DOLADE well extracts optimal parameters for each PV cell model and brought great competition in terms of accuracy, reliability, and computational efficiency in solving the problem.