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发电机是风电机组中的关键部件,然而由于运行环境恶劣、内部结构复杂,发电机发生故障的概率较高且维修困难.针对此问题,提出了一种基于SCADA(supervisory control and data acquisition)数据的发电机健康状况的评估方法.首先结合专家经验并分析状态变量间的相关性,识别出与发电机运行状态具有较强关系的变量和冗余变量,在此基础上进行合理的特征选择.然后利用历史多维状态信息,采用发电机健康运行时的数据建立基于高斯混合模型(GMM)的健康基准模型.最后设计一种基于马氏距离的健康衰退指标(HDI)用于评判发电机的健康状况.利用上海电气某风场2016年的SCADA数据对本文方法进行验证,结果表明,该方法可以准确地跟踪发电机运行状态的变化过程,起到了很好的故障早期识别作用且具有普适性.“,”The generator is a crucial component of the wind turbine. However, the failure probability of the generator is high and maintenance is difficult due to its complicated internal structure and harsh operating environment. To solve this problem, we propose a health assessment method for the wind-turbine generator based on supervisory control and data acquisition (SCADA) data. First, we identify variables related to the operating status of the generator and redundant variables based on expert experience and correlation analysis of the state variables. On this basis, we select some reasonable state parameters. Then, using historical data from normal operation, we establish a health benchmark model based on the Gaussian mixture model (GMM). Finally, to evaluate the health status of a generator, we design a health degradation index (HDI) based on the Mahalanobis distance. We verify the effectiveness of our proposed method, and we apply it to 2016 SCADA data from a wind farm of the Shanghai Electric Wind Power Group Co., Ltd. The test results show that the proposed method can accurately track changes in the generator operating status and facilitate early fault identification.In addition, the proposed method is universal in its application.