Research on fault diagnosis of solar photovoltaic module based on CNN-LSTM
The solar photovoltaic industry has developed rapidly in recent years. Accurate diagnosis of the location and type of PV module faults can improve the efficiency of operation and maintenance personnel. In this paper, a deep learning diagnostic model based on convolutional neural networks-long short...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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National Computer System Engineering Research Institute of China
2020-04-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000117758 |
| _version_ | 1819211306600759296 |
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| author | Cheng Qize Chen Zehua Zhang Yunqin Jiang Wenjie Liu Xiaofeng Shen Liang |
| author_facet | Cheng Qize Chen Zehua Zhang Yunqin Jiang Wenjie Liu Xiaofeng Shen Liang |
| author_sort | Cheng Qize |
| collection | DOAJ |
| description | The solar photovoltaic industry has developed rapidly in recent years. Accurate diagnosis of the location and type of PV module faults can improve the efficiency of operation and maintenance personnel. In this paper, a deep learning diagnostic model based on convolutional neural networks-long short term memory(CNN-LSTM) is proposed, which can be used to complete the detection task. In this paper, a fault classification method based on current performance is established. The algorithm firstly designs a feature extraction algorithm based on the layout characteristics of the PV array, and extracts the lateral and vertical features of the PV array current to obtain the spatial and temporal characteristics. The CNN network further extracts the lateral features and compresses the vertical features to solve the problem of single feature types and slow training. Finally, the LSTM neural network is used to complete the fault diagnosis of the PV modules. |
| first_indexed | 2024-12-23T06:24:59Z |
| format | Article |
| id | doaj.art-cb76b70d1c3041ca8d666ccc1c7aeba6 |
| institution | Directory Open Access Journal |
| issn | 0258-7998 |
| language | zho |
| last_indexed | 2024-12-23T06:24:59Z |
| publishDate | 2020-04-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj.art-cb76b70d1c3041ca8d666ccc1c7aeba62022-12-21T17:57:05ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982020-04-01464667010.16157/j.issn.0258-7998.1910663000117758Research on fault diagnosis of solar photovoltaic module based on CNN-LSTMCheng Qize0Chen Zehua1Zhang Yunqin2Jiang Wenjie3Liu Xiaofeng4Shen Liang5College of Data Science，Taiyuan University of Technology，Taiyuan 030001，ChinaCollege of Data Science，Taiyuan University of Technology，Taiyuan 030001，ChinaCollege of Data Science，Taiyuan University of Technology，Taiyuan 030001，ChinaJinneng Clean Energy Co.，Ltd.，Taiyuan 030001，ChinaCollege of Data Science，Taiyuan University of Technology，Taiyuan 030001，ChinaJinneng Clean Energy Co.，Ltd.，Taiyuan 030001，ChinaThe solar photovoltaic industry has developed rapidly in recent years. Accurate diagnosis of the location and type of PV module faults can improve the efficiency of operation and maintenance personnel. In this paper, a deep learning diagnostic model based on convolutional neural networks-long short term memory(CNN-LSTM) is proposed, which can be used to complete the detection task. In this paper, a fault classification method based on current performance is established. The algorithm firstly designs a feature extraction algorithm based on the layout characteristics of the PV array, and extracts the lateral and vertical features of the PV array current to obtain the spatial and temporal characteristics. The CNN network further extracts the lateral features and compresses the vertical features to solve the problem of single feature types and slow training. Finally, the LSTM neural network is used to complete the fault diagnosis of the PV modules.http://www.chinaaet.com/article/3000117758photovoltaic modulefeature extractioncnnlstmfault diagnosis |
| spellingShingle | Cheng Qize Chen Zehua Zhang Yunqin Jiang Wenjie Liu Xiaofeng Shen Liang Research on fault diagnosis of solar photovoltaic module based on CNN-LSTM Dianzi Jishu Yingyong photovoltaic module feature extraction cnn lstm fault diagnosis |
| title | Research on fault diagnosis of solar photovoltaic module based on CNN-LSTM |
| title_full | Research on fault diagnosis of solar photovoltaic module based on CNN-LSTM |
| title_fullStr | Research on fault diagnosis of solar photovoltaic module based on CNN-LSTM |
| title_full_unstemmed | Research on fault diagnosis of solar photovoltaic module based on CNN-LSTM |
| title_short | Research on fault diagnosis of solar photovoltaic module based on CNN-LSTM |
| title_sort | research on fault diagnosis of solar photovoltaic module based on cnn lstm |
| topic | photovoltaic module feature extraction cnn lstm fault diagnosis |
| url | http://www.chinaaet.com/article/3000117758 |
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