基于工况辨识的风电机组主传动系统运行状态监测

陈结; 陈换过; 肖志奇; 解超

doi:10.19912/j.0254-0096.tynxb.2022-1545

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (2) : 77-85. DOI: 10.19912/j.0254-0096.tynxb.2022-1545

基于工况辨识的风电机组主传动系统运行状态监测

陈结¹, 陈换过¹, 肖志奇², 解超¹

作者信息 +

OPERATIONAL STATE MONITORING OF WIND TURBINE MAIN TRANSMISSION SYSTEM BASED ON WORKING CONDITION RECOGNITION

Chen Jie¹, Chen Huanguo¹, Xiao Zhiqi², Xie Chao¹

Author information +

文章历史 +

摘要

针对风电机组主传动系统运行工况复杂多变,导致状态监测系统误报警率高的问题,提出一种基于工况辨识的风电机组主传动系统运行状态监测方法。首先采用最大互信息系数（MIC）选取数据采集与监视控制系统（SCADA）特征参数,运用k-均值聚类算法对主传动系统运行工况进行划分,解决机组运行状态复杂多变导致运行工况不清的问题;接着提取状态监测系统（CMS）特征参数,并采用层次分析法计算CMS特征参量相对权重,提出主传动系统运行状态评价指标与量化算法;最后利用核密度估计（KDE）方法确定阈值,根据阈值与运行状态指标的关系实现主传动系统异常监测。将该方法运用于实际风场,结果表明所提方法监测结果与实际相符。

Abstract

Aiming at the problem of high false alarm rate of the condition monitoring system caused by complex and changeable operating conditions of the wind turbine main transmission system, a method for monitoring the operational state of the wind turbine main transmission system based on working condition recognition is proposed. Firstly, in order to solve the problem of unclear operating conditions due to complex and variable operational state of wind turbines, the SCADA feature parameters are selected by the maximal information coefficient （MIC）, and the operating conditions of the main transmission system are classified by the k-means clustering algorithm. Then the CMS feature parameters are extracted, and the relative weights of CMS feature parameters are calculated by analytic hierarchy process （AHP）, and the evaluation index and quantification algorithm of the main transmission system operating condition is proposed. Finally, the threshold value is determined using the kernel density estimation （KDE） method, and the abnormality monitoring of the main transmission system is realized according to the relationship between the threshold value and the operating condition index. The proposed method is applied to a wind farm, and the experimental results show that the monitoring results are consistent with the actual situation.

导出引用

陈结, 陈换过, 肖志奇, 解超. 基于工况辨识的风电机组主传动系统运行状态监测[J]. 太阳能学报. 2024, 45(2): 77-85 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1545

Chen Jie, Chen Huanguo, Xiao Zhiqi, Xie Chao. OPERATIONAL STATE MONITORING OF WIND TURBINE MAIN TRANSMISSION SYSTEM BASED ON WORKING CONDITION RECOGNITION[J]. Acta Energiae Solaris Sinica. 2024, 45(2): 77-85 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1545

中图分类号： TH17

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