Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network Model
With increasing the installation of the photovoltaic modules, the different failures on components of the system are dramatically increased and thus lead to a direct impact on system productivity and efficiency. Fault detection and diagnosis accurately have an extreme impact on the maintenance and r...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9285268/ |
| _version_ | 1818349825739980800 |
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| author | Honglu Zhu Sayed Ahmed Zaki Ahmed Mohammed Ahmed Alfakih Mohamed Abdelkarim Abdelbaky Ahmed Rabee Sayed Mubaarak Abdulrahman Abdu Saif |
| author_facet | Honglu Zhu Sayed Ahmed Zaki Ahmed Mohammed Ahmed Alfakih Mohamed Abdelkarim Abdelbaky Ahmed Rabee Sayed Mubaarak Abdulrahman Abdu Saif |
| author_sort | Honglu Zhu |
| collection | DOAJ |
| description | With increasing the installation of the photovoltaic modules, the different failures on components of the system are dramatically increased and thus lead to a direct impact on system productivity and efficiency. Fault detection and diagnosis accurately have an extreme impact on the maintenance and reliability of photovoltaic array. Moreover, for detecting the different faults, selecting the proper indicators for monitoring the system improves the fault diagnosis techniques performance and avoids the complexity of the system. In this paper, an efficient detection and diagnosis model for different fault types is proposed. This model has sequential steps. Firstly, the performance of seven indicators is initially analyzed to predict accurately the nonlinear output behavior of the photovoltaic system under changing environmental conditions, hence select the minimum indicators to detect the typical faults. Secondly, ten fault cases, considering single-fault types and another three faults considering multi-fault types, are investigated. At the same time, the impact of these faults on the selected indicators is deeply analyzed. Finally, the typical fault types are classified and detected effectively depending on three sequential probabilistic neural network models, which give a precise classification of the data inputs. Both theoretical and experimental tests are operated to validate the performance and effectiveness of the proposed model. |
| first_indexed | 2024-12-13T18:12:07Z |
| format | Article |
| id | doaj.art-973868ddd2a74cffb4400ac7bdb26a24 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:12:07Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-973868ddd2a74cffb4400ac7bdb26a242022-12-21T23:35:56ZengIEEEIEEE Access2169-35362020-01-01822050722052210.1109/ACCESS.2020.30431299285268Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network ModelHonglu Zhu0https://orcid.org/0000-0001-9817-1120Sayed Ahmed Zaki Ahmed1https://orcid.org/0000-0001-7545-0039Mohammed Ahmed Alfakih2https://orcid.org/0000-0002-8025-1867Mohamed Abdelkarim Abdelbaky3https://orcid.org/0000-0001-9756-503XAhmed Rabee Sayed4https://orcid.org/0000-0001-5855-7125Mubaarak Abdulrahman Abdu Saif5https://orcid.org/0000-0002-5120-6792School of New Energy, North China Electric Power University, Beijing, ChinaSchool of New Energy, North China Electric Power University, Beijing, ChinaSchool of New Energy, North China Electric Power University, Beijing, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, ChinaDepartment of Electrical Power Engineering, Faculty of Engineering, Cairo University, Giza, EgyptSchool of New Energy, North China Electric Power University, Beijing, ChinaWith increasing the installation of the photovoltaic modules, the different failures on components of the system are dramatically increased and thus lead to a direct impact on system productivity and efficiency. Fault detection and diagnosis accurately have an extreme impact on the maintenance and reliability of photovoltaic array. Moreover, for detecting the different faults, selecting the proper indicators for monitoring the system improves the fault diagnosis techniques performance and avoids the complexity of the system. In this paper, an efficient detection and diagnosis model for different fault types is proposed. This model has sequential steps. Firstly, the performance of seven indicators is initially analyzed to predict accurately the nonlinear output behavior of the photovoltaic system under changing environmental conditions, hence select the minimum indicators to detect the typical faults. Secondly, ten fault cases, considering single-fault types and another three faults considering multi-fault types, are investigated. At the same time, the impact of these faults on the selected indicators is deeply analyzed. Finally, the typical fault types are classified and detected effectively depending on three sequential probabilistic neural network models, which give a precise classification of the data inputs. Both theoretical and experimental tests are operated to validate the performance and effectiveness of the proposed model.https://ieeexplore.ieee.org/document/9285268/Photovoltaicfault detection and classificationmulti-fault casesfeatures calculationsequential probabilistic neural network |
| spellingShingle | Honglu Zhu Sayed Ahmed Zaki Ahmed Mohammed Ahmed Alfakih Mohamed Abdelkarim Abdelbaky Ahmed Rabee Sayed Mubaarak Abdulrahman Abdu Saif Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network Model IEEE Access Photovoltaic fault detection and classification multi-fault cases features calculation sequential probabilistic neural network |
| title | Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network Model |
| title_full | Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network Model |
| title_fullStr | Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network Model |
| title_full_unstemmed | Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network Model |
| title_short | Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network Model |
| title_sort | photovoltaic failure diagnosis using sequential probabilistic neural network model |
| topic | Photovoltaic fault detection and classification multi-fault cases features calculation sequential probabilistic neural network |
| url | https://ieeexplore.ieee.org/document/9285268/ |
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