Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model
The fault prognostics of the photovoltaic (PV) power generation system is expected to be a significant challenge as more and more PV systems with increasingly large capacities continue to come into existence. The PV inverter is the core component of the PV system, and it is essential to develop appr...
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MDPI AG
2020-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/18/4901 |
| _version_ | 1797553216574980096 |
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| author | Zhenyu He Xiaochen Zhang Chao Liu Te Han |
| author_facet | Zhenyu He Xiaochen Zhang Chao Liu Te Han |
| author_sort | Zhenyu He |
| collection | DOAJ |
| description | The fault prognostics of the photovoltaic (PV) power generation system is expected to be a significant challenge as more and more PV systems with increasingly large capacities continue to come into existence. The PV inverter is the core component of the PV system, and it is essential to develop approaches that accurately predict the occurrence of inverter faults to ensure the PV system’s safety. This paper proposes a fault prognostics method which makes full use of the similarities between inverter clusters. First, a feature space was constructed using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Then, the fast clustering algorithm was used to search the center inverter of each sampling time from the feature space. The status of the center inverter was adopted to establish the health baseline. Finally, the Gaussian mixture model was established with two data clusters based on the central inverter and the inverter to be predicted. The divergence of the two clusters could be used to predict the inverter’s fault. The performance of the proposed method was evaluated with real PV monitoring data. The experimental results showed that the proposed method successfully predicted the occurrence of an inverter fault 3 months in advance. |
| first_indexed | 2024-03-10T16:13:13Z |
| format | Article |
| id | doaj.art-0890e1a03fd04ddabb2da0f337f5d90c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T16:13:13Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0890e1a03fd04ddabb2da0f337f5d90c2023-11-20T14:16:26ZengMDPI AGEnergies1996-10732020-09-011318490110.3390/en13184901Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture ModelZhenyu He0Xiaochen Zhang1Chao Liu2Te Han3Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, ChinaState Grid Electric Power Research Institute, Nanjing 211106, ChinaDepartment of Energy and Power Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Energy and Power Engineering, Tsinghua University, Beijing 100084, ChinaThe fault prognostics of the photovoltaic (PV) power generation system is expected to be a significant challenge as more and more PV systems with increasingly large capacities continue to come into existence. The PV inverter is the core component of the PV system, and it is essential to develop approaches that accurately predict the occurrence of inverter faults to ensure the PV system’s safety. This paper proposes a fault prognostics method which makes full use of the similarities between inverter clusters. First, a feature space was constructed using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Then, the fast clustering algorithm was used to search the center inverter of each sampling time from the feature space. The status of the center inverter was adopted to establish the health baseline. Finally, the Gaussian mixture model was established with two data clusters based on the central inverter and the inverter to be predicted. The divergence of the two clusters could be used to predict the inverter’s fault. The performance of the proposed method was evaluated with real PV monitoring data. The experimental results showed that the proposed method successfully predicted the occurrence of an inverter fault 3 months in advance.https://www.mdpi.com/1996-1073/13/18/4901fault prognosticsphotovoltaic inverterGaussian mixture modelJensen–Shannon divergencefast clustering algorithm |
| spellingShingle | Zhenyu He Xiaochen Zhang Chao Liu Te Han Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model Energies fault prognostics photovoltaic inverter Gaussian mixture model Jensen–Shannon divergence fast clustering algorithm |
| title | Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model |
| title_full | Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model |
| title_fullStr | Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model |
| title_full_unstemmed | Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model |
| title_short | Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model |
| title_sort | fault prognostics for photovoltaic inverter based on fast clustering algorithm and gaussian mixture model |
| topic | fault prognostics photovoltaic inverter Gaussian mixture model Jensen–Shannon divergence fast clustering algorithm |
| url | https://www.mdpi.com/1996-1073/13/18/4901 |
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