Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms - Wang, Long; Chen, Zhuo; Guo, Yinyuan; Hu, Weidong; Chang, Xucheng; Wu, Peng; Han, Cong; Li, Jianwei (Frontiers in Energy Research, 2021)
Accurate solar cell modelling is essential for reliable performance evaluation and prediction, real-time control, and maximum power harvest of PV systems. Nevertheless, such problem 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 conventional meta-heuristic algorithms difficult to obtain a high-quality optimum for solar cell modelling without a reliable fitness function. To address these problems, a novel genetic neural network (GNN) based parameter estimation strategy for solar cell is proposed. Based on measured I-V data, GNN firstly accomplishes the training of neural network via 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 reliable fitness function. Finally, two different cell models, e.g., single diode model (SDM) and double diode model (DDM are employed to validate the feasibility of GNN. Case studies verify that GNN based meta-heuristic algorithms can efficiently improve modelling reliability and convergence rate compared against meta-heuristic algorithms using only original measured I-V data.