摘要
主轴承故障诊断是提升风力发电机可靠性和可用性的重要手段。然而在海上风电场建立初期,难以在每台机组上都获取到足够多的训练样本和全面的运行数据,因此,利用不同机组之间结构和原理的高度相似性,通过迁移学习方法得到一个可以适用于多台风电机组的可靠的主轴承故障预警模型是解决问题的关键。该文提出一种基于XGBoost和迁移学习的综合方法来对风电场中不同风电机组进行主轴承故障预警。首先,采集广东某海上风电场6.8 MW风电机组的数据,构建出一套基于XGBoost的主轴承故障预警模型,然后再将其迁移至其他不同的风电机组中。实验表明,与LSTM、GRU、Light和Random forest等模型相比,本文构建的模型的准确率最高,R2高达0.995。并且在经过迁移后仍然保持了较高准确率,各机组的R 2均大于0.95。
Fault diagnosis of main bearings is an important means to improve the reliability and availability of wind turbines.However,it is difficult to obtain sufficient training samples and comprehensive operational data from each unit in the early stages of wind farm establishment.Therefore,the key to solve the problem is to obtain a reliable main bearing fault warning model applicable to multiple wind turbines through transfer learning methods by taking advantage of the high similarity of structure and principle between different units.In this paper,a comprehensive method based on XGBoost and transfer learning is proposed to conduct main bearing fault early warning for different wind turbines in wind farms.Firstly,the data of a 6.8 MW wind turbine in a offshore wind farm in Guangdong province is collected,to construct a set of main bearing fault warning model based on XGBoost,and then it is transferred to other wind turbines.Experiments show that compared with LSTM,GRU,Light and Random forest models,the model constructed in this paper has the highest accuracy,with an R 2 as high as 0.995.And after transfer,it still maintains a high accuracy,the R 2 of each unit is greater than 0.95.
作者
马士东
龚文斌
万安平
MA Shidong;GONG Wenbin;WAN Anping(Guangdong Huadian Fuxin Yangjiang Offshore Windpower Co.,Ltd.,Yangjiang 529500,Guangdong China;Department of Mechanical Engineering,Hangzhou City University,Hangzhou 310015,Zhejiang,China)
出处
《电力大数据》
2024年第4期16-24,共9页
Power Systems and Big Data
基金
广东省海洋经济发展(海洋六大产业)专项资金项目(GDNRC〔2022〕28)。
