Identifying the anomalies of wind turbine (WT) and maintaining in time will improve the reliability of wind turbine and the efficiency of energy use, however it is difficult toidentify the wind turbine’s abnormal operation by the traditional threshold settings because the anomalies can be induced by multiple factors.Therefore, this paper presents an anomaly identification model for wind turbine state parameters,and the model can identify abnormal state which the fluctuation range of the condition parametersis within the SCADA alarm threshold. The main work is as follows: 1) in order to increase the accuracy of the prediction model, a novel BPNN model integrated genetic algorithm (GA) was employed to optimize the training method (called GABP method), data samples, and input parameter selection, respectively; 2) on this basis, the distribution characteristics of state parameter prediction errors were depicted by a T-location scale (TLS) distribution with the shift factor and elastic coefficient; 3)error abnormal index (EAI) is defined to quantify the abnormal level of the prediction error, which is used as an indicator of the wind turbine anomaly. The proposed method has been applied on areal 1.5 MW wind turbine, and the analysis shows that the proposed method is effective in wind turbine anomaly identification.

识别风力涡轮机（WT）的异常并及时进行维护将提高风力涡轮机的可靠性和能源使用效率，但是通过传统的阈值设置很难识别风力涡轮机的异常运行，因为异常可能是由多个原因引起的。因此，本文提出了一种风机状态参数异常识别模型，该模型可以识别出状态参数波动范围在SCADA报警阈值内的异常状态。主要工作如下：1）为了提高预测模型的准确性，采用了一种新的BPNN模型集成遗传算法（GA）对训练方法（称为GABP方法），数据样本和输入参数选择进行了优化。 ， 分别; 2）在此基础上，状态参数预测误差的分布特征由带有位移因子和弹性系数的T位置尺度（TLS）分布描述。3）错误异常指数（EAI）被定义为量化预测错误的异常水平，其被用作风力涡轮机异常的指标。将该方法应用于1.5MW风电机组，分析表明该方法在风电机组异常识别中是有效的。