Abstract
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.
Highlights
In recent years, due to rapid fossil fuel depletion (Peng et al, 2020), booming global energy demand (Shangguan et al, 2020a), and a series of severe eco-environmental problems (Yang et al, 2015), concepts of sustainable development and an environmentally friendly society are receiving increasingly widespread attention (Shangguan et al, 2020b)
For the diode model (DDM), the average Root mean square error (RMSE) achieved via seven different algorithms with different measured datasets in 80 runs is illustrated in Table 2, which indicates that increased prediction data generated by the genetic neural network (GNN) can effectively improve calculation accuracy and stability
Radars of average RMSE achieved via each meta-heuristic algorithm with six groups of data at different scales are provided in Figures 12, 13, which show that average RMSE acquired via all algorithms with GNNbased data prediction are smaller compared with that obtained without data prediction at different scales of data, especially under 50% data
Summary
Due to rapid fossil fuel depletion (Peng et al, 2020), booming global energy demand (Shangguan et al, 2020a), and a series of severe eco-environmental problems (Yang et al, 2015), concepts of sustainable development and an environmentally friendly society are receiving increasingly widespread attention (Shangguan et al, 2020b). To obtain optimal parameters of the ANN, various methods are employed to train networks, such as the Newton-Raphson method (Soloway and Haley, 1996) and gradient descent method (Noriega and Wang, 1998) These methods essentially belong to gradient-based optimization, which result in a low-quality optimum or a complex computation (Song et al, 2007) as their performance highly depends on neural network structure, complexity of cost function, and so on. This paper develops novel genetic neural network (GNN)-based meta-heuristic algorithms for solar cell accurate modeling, which have the following contributions:. GNN-based meta-heuristic algorithms can implement an efficient search for PV cell parameter estimation, which can acquire a higher-quality optimum than conventional metaheuristic algorithms with only inadequate measured I-V data;. Seven parameters need to be identified for the DDM, e.g., Iph, Isd, Isd, Rs, Rsh, a1, and a2
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