Statistics

Algorithm

Electrical engineering

Physics

Mathematical optimization

Engineering

Mann–Whitney U test

Local optimum

Sociology

Demography

Nonlinear system

Estimation theory

Wilcoxon signed-rank test

Population

Mathematics

Computer science

Photovoltaic system

Solar energy

Quantum mechanics

Differential evolution

Energy Conversion and Management

Photovoltaic (PV) generation systems are vital to the utilization of the sustainable and pollution-free solar energy. However, the parameter estimation of PV systems remains very challenging due to its inherent nonlinear, multi-variable, and multi-modal characteristics. In this paper, we propose a state-of-the-art optimization method, namely, directional permutation differential evolution algorithm (DPDE), to tackle the parameter estimation of several kinds of solar PV models. By fully utilizing the information arisen from the search population and the direction of differential vectors, DPDE can possess a strong global exploration ability of jumping out of the local optima. To verify the performance of DPDE, six groups of experiments based on single, double, triple diode models and PV module models are conducted. Extensive comparative results between DPDE and other fifteen representative algorithms show that DPDE outperforms its peers in terms of the solution accuracy. Additionally, statistical results based on Wilcoxon rank-sum and Friedman tests indicate that DPDE is the most robust and best-performing algorithm for the parameter estimation of PV systems.

A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models