Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network Marker
With the rapid increase in the installed capacity of wind power in China, early warning and identification of defects in wind turbine units has become crucial for the healthy development of the wind power industry. This paper proposes a comprehensive fault warning method for the entire wind turbine...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10763496/ |
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| _version_ | 1841557017616973824 |
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| author | Zesheng Pan Ruiming Fang Tingyu Wei Rongyan Shang Changqing Peng |
| author_facet | Zesheng Pan Ruiming Fang Tingyu Wei Rongyan Shang Changqing Peng |
| author_sort | Zesheng Pan |
| collection | DOAJ |
| description | With the rapid increase in the installed capacity of wind power in China, early warning and identification of defects in wind turbine units has become crucial for the healthy development of the wind power industry. This paper proposes a comprehensive fault warning method for the entire wind turbine unit that is distinct from most current methods that focus solely on partial components. It addresses the complex coupling between various monitoring data from the supervisory control and data acquisition system and the internal defects of the unit. Utilizing a sample covariance matrix and the dynamic network marker theory, the method integrates diverse data inputs to predict potential failures. Case validation reveals that this approach maintains robustness across different operating conditions, issues warnings approximately 6 hours and 40 minutes before faults occur, and effectively identifies fault types. This capability is beneficial for initiative maintenance and rational planning of maintenance schedules in wind turbines. |
| format | Article |
| id | doaj-art-6456be985d1f447a800037b28eb0260b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6456be985d1f447a800037b28eb0260b2025-01-07T00:02:33ZengIEEEIEEE Access2169-35362025-01-01132474248510.1109/ACCESS.2024.350454310763496Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network MarkerZesheng Pan0https://orcid.org/0009-0003-0317-0881Ruiming Fang1https://orcid.org/0000-0001-8841-6024Tingyu Wei2https://orcid.org/0009-0009-1608-1363Rongyan Shang3Changqing Peng4School of Information Science and Engineering, Huaqiao University, Xiamen, ChinaSchool of Information Science and Engineering, Huaqiao University, Xiamen, ChinaSchool of Information Science and Engineering, Huaqiao University, Xiamen, ChinaSchool of Information Science and Engineering, Huaqiao University, Xiamen, ChinaSchool of Information Science and Engineering, Huaqiao University, Xiamen, ChinaWith the rapid increase in the installed capacity of wind power in China, early warning and identification of defects in wind turbine units has become crucial for the healthy development of the wind power industry. This paper proposes a comprehensive fault warning method for the entire wind turbine unit that is distinct from most current methods that focus solely on partial components. It addresses the complex coupling between various monitoring data from the supervisory control and data acquisition system and the internal defects of the unit. Utilizing a sample covariance matrix and the dynamic network marker theory, the method integrates diverse data inputs to predict potential failures. Case validation reveals that this approach maintains robustness across different operating conditions, issues warnings approximately 6 hours and 40 minutes before faults occur, and effectively identifies fault types. This capability is beneficial for initiative maintenance and rational planning of maintenance schedules in wind turbines.https://ieeexplore.ieee.org/document/10763496/Fault detectionfault diagnosisnetwork theorypredictive maintenancerenewable energy systemssupervisory control and data acquisition |
| spellingShingle | Zesheng Pan Ruiming Fang Tingyu Wei Rongyan Shang Changqing Peng Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network Marker IEEE Access Fault detection fault diagnosis network theory predictive maintenance renewable energy systems supervisory control and data acquisition |
| title | Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network Marker |
| title_full | Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network Marker |
| title_fullStr | Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network Marker |
| title_full_unstemmed | Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network Marker |
| title_short | Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network Marker |
| title_sort | fault diagnosis of wind turbines based on improved dynamic network marker |
| topic | Fault detection fault diagnosis network theory predictive maintenance renewable energy systems supervisory control and data acquisition |
| url | https://ieeexplore.ieee.org/document/10763496/ |
| work_keys_str_mv | AT zeshengpan faultdiagnosisofwindturbinesbasedonimproveddynamicnetworkmarker AT ruimingfang faultdiagnosisofwindturbinesbasedonimproveddynamicnetworkmarker AT tingyuwei faultdiagnosisofwindturbinesbasedonimproveddynamicnetworkmarker AT rongyanshang faultdiagnosisofwindturbinesbasedonimproveddynamicnetworkmarker AT changqingpeng faultdiagnosisofwindturbinesbasedonimproveddynamicnetworkmarker |