Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey
Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/22/8693 |
| _version_ | 1827644505422036992 |
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| author | Zixia Yuan Guojiang Xiong Xiaofan Fu |
| author_facet | Zixia Yuan Guojiang Xiong Xiaofan Fu |
| author_sort | Zixia Yuan |
| collection | DOAJ |
| description | Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis. |
| first_indexed | 2024-03-09T18:21:29Z |
| format | Article |
| id | doaj.art-64437e242da84c149f3ba76a99f9822c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T18:21:29Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-64437e242da84c149f3ba76a99f9822c2023-11-24T08:17:02ZengMDPI AGEnergies1996-10732022-11-011522869310.3390/en15228693Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A SurveyZixia Yuan0Guojiang Xiong1Xiaofan Fu2Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaGuizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaGuizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaSolar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis.https://www.mdpi.com/1996-1073/15/22/8693artificial intelligencefault diagnosisneural networkphotovoltaicsolar energyreview |
| spellingShingle | Zixia Yuan Guojiang Xiong Xiaofan Fu Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey Energies artificial intelligence fault diagnosis neural network photovoltaic solar energy review |
| title | Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey |
| title_full | Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey |
| title_fullStr | Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey |
| title_full_unstemmed | Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey |
| title_short | Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey |
| title_sort | artificial neural network for fault diagnosis of solar photovoltaic systems a survey |
| topic | artificial intelligence fault diagnosis neural network photovoltaic solar energy review |
| url | https://www.mdpi.com/1996-1073/15/22/8693 |
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