基于XGBoost和Wasserstein距离的风电机组塔架振动监测研究  

RESEARCH ON WIND TURBINE TOWER VIBRATION MONITORING BASED ON XGBOOST AND WASSERSTEIN DISTANCE

作　　者：苏连成[1] 朱娇娇 郭高鑫 李英伟[3] Su Liancheng;Zhu Jiaojiao;Guo Gaoxin;Li Yingwei(School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China;China Heavy Machinery Research Institute Co.,Ltd.,Xi’an 710018,China;School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China)

机构地区：[1]燕山大学电气工程学院,秦皇岛066004 [2]中国重型机械研究院股份公司,西安710018 [3]燕山大学信息工程学院,秦皇岛066004

出　　处：《太阳能学报》2023年第1期306-312,共7页Acta Energiae Solaris Sinica

基　　金：国防基础研究计划(JCKY2019407C002)。

摘　　要：针对风电机组塔架振动监测问题,考虑到风能脉动与机组控制动作等激励对塔架振动的影响,提出一种基于数据驱动的塔架振动监测方法。首先基于K-均值算法对风电机组工况进行辨识,分析各状态参量、机组工况对塔架振动的影响;其次基于极限梯度提升(XGBoost)算法对不同工况下的塔架振动趋势进行建模预测,针对同一风电场不同塔架振动预测残差的差异,提出一种基于Wasserstein距离的塔架振动监测方法;最后使用风电场实际数据验证,以误差平方和为评价指标,考虑机组工况条件的XGBoost预测精度提高了34.6%。基于数据驱动的方法能有效识别风电场中异常振动较频繁的塔架,提升了运维效率。A data-driven tower vibration monitoring method considering the influence of wind pulsation and turbine control action on tower sproposed for solving the problem of tower vibration monitoring.Firstly,the operation conditions of wind turbine are identified based on K-means clustering,and the effects of various state parameters and operation conditions on tower are analyzed.Secondly,the tower vibration trend under different operation conditions is modeled and predicted based on XGBoost algorithm;a tower vibration monitoring method based on Wasserstein distance is proposed according to the difference of preicted residual errors of different towers in the same wind farm.Finally,the actual data of the wind farm is used for verification,taking the sum of squared error as the evaluation index,and the results show that the method considering turbine operation conditions improves the accuracy of the tower vibration prediction by 34.6%.The data-driven method effectively identifies the tower with frequent abnormal vibration in the wind farm and improves the efficiency of operation and maintenance.

关 键 词：风电机组 塔架振动 数据驱动 XGBoost Wasserstein距离 

分 类 号：TK513.5[动力工程及工程热物理—热能工程]