A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance
To overcome the problems of wind turbine (WT) degradation assessment, a new kernel entropy method based on supervisory control and data acquisition (SCADA) was proposed. This approach can be used to effectively monitor and assess WT performance degradation. First, a new condition monitoring method b...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/395157 |
| _version_ | 1797206819540566016 |
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| author | Yong-Sheng Qi* Chao Ren Xue-Jin Gao Li-Qiang Liu Chao-Yi Dong |
| author_facet | Yong-Sheng Qi* Chao Ren Xue-Jin Gao Li-Qiang Liu Chao-Yi Dong |
| author_sort | Yong-Sheng Qi* |
| collection | DOAJ |
| description | To overcome the problems of wind turbine (WT) degradation assessment, a new kernel entropy method based on supervisory control and data acquisition (SCADA) was proposed. This approach can be used to effectively monitor and assess WT performance degradation. First, a new condition monitoring method based on a kernel entropy component analysis (KECA) was developed for nonlinear data. Then, the squared prediction error (SPE) was used to monitor the WT health state. Due to the diversity and nonlinearity of SCADA data, fault features are easily overwhelmed by other vibration signals. To address this, a new kernel entropy partial least squares (KEPLS) algorithm was introduced. The proposed kernel entropy method improves the performance prediction by considering higher order information. Furthermore, changes in the prediction residual can be used to define certain limits to realize early warning of WT faults. Finally, the method was applied to actual SCADA data of a wind farm. The results show that the method can accurately evaluate the health state of WTs, thus verifying the effectiveness and feasibility of the proposed method. |
| first_indexed | 2024-04-24T09:13:05Z |
| format | Article |
| id | doaj.art-be982b227f9d44a3b32dd8aac09fd122 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:13:05Z |
| publishDate | 2022-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-be982b227f9d44a3b32dd8aac09fd1222024-04-15T17:35:34ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392022-01-0129266467510.17559/TV-20210102034143A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation PerformanceYong-Sheng Qi*0Chao Ren1Xue-Jin Gao2Li-Qiang Liu3Chao-Yi Dong4Inner Mongolia University of Technology, School of Electric Power, 49 Aimin Street, Xincheng District, Hohhot, ChinaInner Mongolia University of Technology, School of Electric Power, 49 Aimin Street, Xincheng District, Hohhot, ChinaBeijing University of Technology, School of Information Department, 100 Ping Leyuan, Chaoyang District, Beijing, ChinaInner Mongolia University of Technology, School of Electric Power, 49 Aimin Street, Xincheng District, Hohhot, ChinaInner Mongolia University of Technology, School of Electric Power, 49 Aimin Street, Xincheng District, Hohhot, ChinaTo overcome the problems of wind turbine (WT) degradation assessment, a new kernel entropy method based on supervisory control and data acquisition (SCADA) was proposed. This approach can be used to effectively monitor and assess WT performance degradation. First, a new condition monitoring method based on a kernel entropy component analysis (KECA) was developed for nonlinear data. Then, the squared prediction error (SPE) was used to monitor the WT health state. Due to the diversity and nonlinearity of SCADA data, fault features are easily overwhelmed by other vibration signals. To address this, a new kernel entropy partial least squares (KEPLS) algorithm was introduced. The proposed kernel entropy method improves the performance prediction by considering higher order information. Furthermore, changes in the prediction residual can be used to define certain limits to realize early warning of WT faults. Finally, the method was applied to actual SCADA data of a wind farm. The results show that the method can accurately evaluate the health state of WTs, thus verifying the effectiveness and feasibility of the proposed method.https://hrcak.srce.hr/file/395157degradation performance monitoringhealth assessmentKernel Entropy Component Analysis (KECA)Kernel Entropy Partial Least Squares (KEPLS)SCADA data |
| spellingShingle | Yong-Sheng Qi* Chao Ren Xue-Jin Gao Li-Qiang Liu Chao-Yi Dong A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance Tehnički Vjesnik degradation performance monitoring health assessment Kernel Entropy Component Analysis (KECA) Kernel Entropy Partial Least Squares (KEPLS) SCADA data |
| title | A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance |
| title_full | A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance |
| title_fullStr | A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance |
| title_full_unstemmed | A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance |
| title_short | A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance |
| title_sort | kernel entropy method and its application in monitoring and assessment of wind turbine degradation performance |
| topic | degradation performance monitoring health assessment Kernel Entropy Component Analysis (KECA) Kernel Entropy Partial Least Squares (KEPLS) SCADA data |
| url | https://hrcak.srce.hr/file/395157 |
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