Multi-strategy adaptive guidance differential evolution algorithm using fitness-distance balance and opposition-based learning for constrained global optimization of photovoltaic cells and modules

It is of great significance to obtain the parameters of photovoltaic (PV) models quickly and accurately for the efficient operation and maintenance of PV power plants. A multi-strategy adaptive guidance differential evolution (AGDE) algorithm using fitness-distance balance (FDB) and opposition-based learning (OBL) is proposed for constrained global optimization of PV cells and modules. FDB and OBL are added on the basis of AGDE to improve the local search ability of the algorithm, and thus identify the parameters of the PV models faster and more accurately. Among the two improved strategies, FDB is a recently developed powerful method that can efficiently model selection processes in nature. In this study, the mutation mating pool of the AGDE algorithm is redesigned using the FDB method. OBL is adopted to increases the initial population diversity of AGDE. In order to verify the performance of the proposed FDB-AGDE in the parameter estimation for PV models, the experimental verification is carried out on two PV cells and three PV modules. The experimental results show that FDB-OADE has better accuracy and robustness in photovoltaic identification compared with other algorithms.