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, and maximum power harvest of photovoltaic (PV) systems. Nevertheless, such a model cannot always achieve satisfactory performance based on conventional optimization strategies caused by i...
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Frontiers Media S.A.
2021-06-01
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doaj-ef870df1347b43cf964d69d5f7abfd222021-06-07T11:01:56ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-06-01910.3389/fenrg.2021.696204696204Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic AlgorithmsLong Wang0Zhuo Chen1Yinyuan Guo2Weidong Hu3Xucheng Chang4Peng Wu5Cong Han6Jianwei Li7Zhengzhou University of Aeronautics, Zhengzhou, ChinaXuchang KETOP Testing Research Institute Co. Ltd., Xuchang, ChinaXuchang KETOP Testing Research Institute Co. Ltd., Xuchang, ChinaXuchang KETOP Testing Research Institute Co. Ltd., Xuchang, ChinaZhengzhou University of Aeronautics, Zhengzhou, ChinaZhengzhou University of Aeronautics, Zhengzhou, ChinaXuchang KETOP Testing Research Institute Co. Ltd., Xuchang, ChinaXJ Group Corporation, Xuchang, ChinaAccurate 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.https://www.frontiersin.org/articles/10.3389/fenrg.2021.696204/fullparameter estimationsolar cellgenetic neural networkdata predictionmeta-heuristic algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Long Wang Zhuo Chen Yinyuan Guo Weidong Hu Xucheng Chang Peng Wu Cong Han Jianwei Li |
spellingShingle |
Long Wang Zhuo Chen Yinyuan Guo Weidong Hu Xucheng Chang Peng Wu Cong Han Jianwei Li Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms Frontiers in Energy Research parameter estimation solar cell genetic neural network data prediction meta-heuristic algorithm |
author_facet |
Long Wang Zhuo Chen Yinyuan Guo Weidong Hu Xucheng Chang Peng Wu Cong Han Jianwei Li |
author_sort |
Long Wang |
title |
Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms |
title_short |
Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms |
title_full |
Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms |
title_fullStr |
Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms |
title_full_unstemmed |
Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms |
title_sort |
accurate solar cell modeling via genetic neural network-based meta-heuristic algorithms |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Energy Research |
issn |
2296-598X |
publishDate |
2021-06-01 |
description |
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. |
topic |
parameter estimation solar cell genetic neural network data prediction meta-heuristic algorithm |
url |
https://www.frontiersin.org/articles/10.3389/fenrg.2021.696204/full |
work_keys_str_mv |
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