Using Transfer Learning and XGBoost for Early Detection of Fires in Offshore Wind Turbine Units

Anping Wan, Chenyu Du, Wenbin Gong, Chao Wei (), Khalil AL-Bukhaiti (), Yunsong Ji, Shidong Ma, Fareng Yao and Lizheng Ao
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Anping Wan: Department of Mechanical Engineering, Hangzhou City University, Hangzhou 310015, China
Chenyu Du: Department of Mechanical Engineering, Hangzhou City University, Hangzhou 310015, China
Wenbin Gong: Department of Mechanical Engineering, Hangzhou City University, Hangzhou 310015, China
Chao Wei: Huadian Electric Power Research Institute, Hangzhou 310030, China
Khalil AL-Bukhaiti: Department of Mechanical Engineering, Hangzhou City University, Hangzhou 310015, China
Yunsong Ji: Guangdong Huadian Fuxin Yangjiang Offshore Wind Power Co., Ltd., Yangjiang 529500, China
Shidong Ma: Guangdong Huadian Fuxin Yangjiang Offshore Wind Power Co., Ltd., Yangjiang 529500, China
Fareng Yao: Guangdong Huadian Fuxin Yangjiang Offshore Wind Power Co., Ltd., Yangjiang 529500, China
Lizheng Ao: Guangdong Huadian Fuxin Yangjiang Offshore Wind Power Co., Ltd., Yangjiang 529500, China

Energies, 2024, vol. 17, issue 10, 1-20

Abstract: To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine operation data in a SCADA system. It uses an extreme gradient-boosting tree (XGBoost) algorithm to build an offshore wind turbine unit fire warning model with a multiparameter prediction function. This paper selects some parameters from the dataset as input variables for the model, with average cabin temperature, average outdoor temperature, average cabin humidity, and average atmospheric humidity as output variables. This paper analyzes the distribution information of input and output variables and their correlation, analyzes the predicted difference, and then provides an early warning for wind turbine fires. This paper uses this fire warning model to transfer learning to different models of offshore wind turbines in the same wind farm to achieve fire warning. The experimental results show that the prediction performance of the multiparameter is accurate, with an average MAPE of 0.016 and an average RMSE of 0.795. It is better than the average MAPE (0.051) and the average RMSE (2.020) of the prediction performance of a backpropagation (BP) neural network, as well as the average MAPE (0.030) and the average RMSE (1.301) of the prediction performance of random forest. The transfer learning model has good prediction performance, with an average MAPE of 0.022 and an average RMSE of 1.469.

Keywords: transfer learning; XGBoost algorithm; offshore wind turbines; data-driven; fire warning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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