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Condition monitoring for multiple wind turbines based on balanced distribution adaptive transfer learning
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Yajie Zhang1, 2, Luo Wang3, Yulu Liu1, 2, Bo Yue3, Shuang Han1, 2, Ying Su3, Yongqian Liu1, 2
Renewable Energy Resources | 2024, 42(8) : 1068 - 1073
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Renewable Energy Resources | 2024, 42(8): 1068-1073
Condition monitoring for multiple wind turbines based on balanced distribution adaptive transfer learning
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Yajie Zhang1, 2, Luo Wang3, Yulu Liu1, 2, Bo Yue3, Shuang Han1, 2, Ying Su3, Yongqian Liu1, 2
Affiliations
  • 1 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing 102206 China
  • 2 School of New Energy North China Electric Power University Beijing 102206 China
  • 3 China Three Gorges Corporation Beijing 100038 China
Published: 2024-08-20
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Accurate condition monitoring of wind turbines is crucial to the safe and stable operation of wind turbines and the improvement of economic benefits. However, affected by the divergence in the distribution of operating data of different wind turbines, the existing condition monitoring methods have the problem of difficulty in taking into account the accuracy and efficiency in the application scenario of multiple wind turbines. BDA can shorten the data distance and reduce the data distribution divergence. Therefore, this paper propose a multiwind turbine condition monitoring method based on balanced distribution adaptive transfer learning. Firstly, the mutual information method based on Copula entropy is used to mine the key influencing parameters of the wind turbine condition; then, a wind turbine condition monitoring model is established based on the GRU model and SPRT method; wind turbine operation data distribution assimilation model based on BDA is constructed, and used for multiwind turbine condition monitoring. Results show that the proposed method can effectively save the modeling cost and calculation cost, and can significantly improve the monitoring efficiency on the premise of ensuring the monitoring accuracy of the operating state of multiple wind turbines.

wind turbine  /  condition monitor  /  balanced distribution adaptive transfer learning  /  sequential probability ratio test  /  gated recurrent unit
Yajie Zhang, Luo Wang, Yulu Liu, Bo Yue, Shuang Han, Ying Su, Yongqian Liu. Condition monitoring for multiple wind turbines based on balanced distribution adaptive transfer learning[J]. Renewable Energy Resources, 2024 , 42 (8) : 1068 -1073 .
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Article Info
  • Receive Date：2022-10-27
  • Online Date：2025-07-22
  • Published：2024-08-20
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  • Received：2022-10-27
Funding
Affiliations
    1 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing 102206 China
    2 School of New Energy North China Electric Power University Beijing 102206 China
    3 China Three Gorges Corporation Beijing 100038 China
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表12种不同金属材料的力学参数
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Number of
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Number of
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Percentage of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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