摘要：Accurate solar cell modeling is essential for reliable performance evaluation and prediction, real-time control, and maximum power harvest of photovoltaic (PV) systems. Nevertheless, such a model cannot always achieve satisfactory performance based on conventional optimization strategies caused by its high-nonlinear characteristics. Moreover, inadequate measured output current-voltage (I-V) data make it difficult for conventional meta-heuristic algorithms to obtain a high-quality optimum for solar cell modeling without a reliable fitness function. To address these problems, a novel genetic neural network (GNN)-based parameter estimation strategy for solar cells is proposed. Based on measured I-V data, the GNN firstly accomplishes the training of the neural network via a genetic algorithm. Then it can predict more virtual I-V data, thus a reliable fitness function can be constructed using extended I-V data. Therefore, meta-heuristic algorithms can implement an efficient search based on the reliable fitness function. Finally, two different cell models, e.g., a single diode model (SDM) and double diode model (DDM) are employed to validate the feasibility of the GNN. Case studies verify that GNN-based meta-heuristic algorithms can efficiently improve modeling reliability and convergence rate compared against meta-heuristic algorithms using only original measured I-V data.
