Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms

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