Research on Online Monitoring of Crack Damage of Wind Turbine Blades Based on Working Modal Analysis
Since crack damage of wind turbine (WT) blades is easy to occur and difficult to find, online monitoring of blade crack damage is carried out by collecting and analyzing blade vibration signals. Firstly, based on the theory of working modal analysis, an online identification method of blade modal pa...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2023-08-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303035 |
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| _version_ | 1850228403457753088 |
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| author | Yuhui WU Yangfan ZHANG Feng GAO Yu WANG Yaohan WANG Weixin YANG Hong ZHANG |
| author_facet | Yuhui WU Yangfan ZHANG Feng GAO Yu WANG Yaohan WANG Weixin YANG Hong ZHANG |
| author_sort | Yuhui WU |
| collection | DOAJ |
| description | Since crack damage of wind turbine (WT) blades is easy to occur and difficult to find, online monitoring of blade crack damage is carried out by collecting and analyzing blade vibration signals. Firstly, based on the theory of working modal analysis, an online identification method of blade modal parameters based on transmissibility is constructed, and a blade vibration physical experiment platform is built for the experimental verification of the method. By comparing the experimental results with the traditional hammer excitation method, the accuracy of the method is verified. Then, with a 5 MW WT as an example, the blade crack damage fault is simulated, and the damage fault characteristics are obtained through working modal analysis. Finally, blade vibration signals, modal parameters, and WT operation data are fused into multi-source data sets, and blade crack damage fault diagnosis is performed based on the LightGBM algorithm. The diagnosis results show that the LightGBM algorithm can achieve a better diagnosis effect than the conventional machine learning algorithm, and the accuracy of the diagnosis algorithm can be significantly increased by integrating blade modal parameters into the data set, so as to improve the accuracy of online monitoring of blade crack damage. |
| format | Article |
| id | doaj-art-3dc97f9d92bb4754b92065ed3d6eca21 |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2023-08-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-3dc97f9d92bb4754b92065ed3d6eca212025-08-20T02:04:33ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-08-01561010611410.11930/j.issn.1004-9649.202303035zgdl-56-10-wuyuhuiResearch on Online Monitoring of Crack Damage of Wind Turbine Blades Based on Working Modal AnalysisYuhui WU0Yangfan ZHANG1Feng GAO2Yu WANG3Yaohan WANG4Weixin YANG5Hong ZHANG6North China Electric Power Research Institute Co., Ltd., Beijing 100089, ChinaNorth China Electric Power Research Institute Co., Ltd., Beijing 100089, ChinaNorth China Electric Power University, Beijing 102206, ChinaNorth China Electric Power Research Institute Co., Ltd., Beijing 100089, ChinaNorth China Electric Power Research Institute Co., Ltd., Beijing 100089, ChinaNorth China Electric Power Research Institute Co., Ltd., Beijing 100089, ChinaNorth China Electric Power University, Beijing 102206, ChinaSince crack damage of wind turbine (WT) blades is easy to occur and difficult to find, online monitoring of blade crack damage is carried out by collecting and analyzing blade vibration signals. Firstly, based on the theory of working modal analysis, an online identification method of blade modal parameters based on transmissibility is constructed, and a blade vibration physical experiment platform is built for the experimental verification of the method. By comparing the experimental results with the traditional hammer excitation method, the accuracy of the method is verified. Then, with a 5 MW WT as an example, the blade crack damage fault is simulated, and the damage fault characteristics are obtained through working modal analysis. Finally, blade vibration signals, modal parameters, and WT operation data are fused into multi-source data sets, and blade crack damage fault diagnosis is performed based on the LightGBM algorithm. The diagnosis results show that the LightGBM algorithm can achieve a better diagnosis effect than the conventional machine learning algorithm, and the accuracy of the diagnosis algorithm can be significantly increased by integrating blade modal parameters into the data set, so as to improve the accuracy of online monitoring of blade crack damage.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303035wind turbineblade crack damageworking modal analysistransmissibilitymachine learning |
| spellingShingle | Yuhui WU Yangfan ZHANG Feng GAO Yu WANG Yaohan WANG Weixin YANG Hong ZHANG Research on Online Monitoring of Crack Damage of Wind Turbine Blades Based on Working Modal Analysis Zhongguo dianli wind turbine blade crack damage working modal analysis transmissibility machine learning |
| title | Research on Online Monitoring of Crack Damage of Wind Turbine Blades Based on Working Modal Analysis |
| title_full | Research on Online Monitoring of Crack Damage of Wind Turbine Blades Based on Working Modal Analysis |
| title_fullStr | Research on Online Monitoring of Crack Damage of Wind Turbine Blades Based on Working Modal Analysis |
| title_full_unstemmed | Research on Online Monitoring of Crack Damage of Wind Turbine Blades Based on Working Modal Analysis |
| title_short | Research on Online Monitoring of Crack Damage of Wind Turbine Blades Based on Working Modal Analysis |
| title_sort | research on online monitoring of crack damage of wind turbine blades based on working modal analysis |
| topic | wind turbine blade crack damage working modal analysis transmissibility machine learning |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303035 |
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