基于SCADA参量关联互信息自编码的风电机组故障检测方法

针对风力发电机组数据采集与监视控制系统(supervisory control and data acquisition,SCADA)监测参量间的耦合关联性,提出基于多参数耦合关联互信息编码的风电机组故障检测方法.该方法构建了SCADA数据的耦合关联矩阵,采用互信息变分自编码器对关联矩阵进行编码重构;将SCADA参量关联矩阵的重构误差作为机组健康评估指标,结合指数加权移动平均模型的迭代更新,对机组实时故障阈值进行自适应设置.两个风场的风电机组SCADA数据分析结果表明,所提方法充分利用了SCADA数据的耦合关联结构信息,能有效提高风电机组故障检测的准确性及对环境工况的鲁棒性.

Wind turbine fault detection based on mutual information auto-encoding of SCADA data correlation

Owing that the monitoring variables of the wind turbine supervisory control and data acquisition(SCADA) system are highly correlated and coupled, a wind turbine fault detection method is proposed based on multi-parameter correlation coupling and mutual information auto-encoder. The correlation matrix of multidimensional time series is established to describe the coupled relationship of SCADA data. The mutual information based variational auto-encoder is proposed for the encoding-decoding reconstruction of the correlation matrix. The wind turbine health evaluation index is constructed based on the reconstruction error of the correlation matrix, and then the early fault threshold is set through the update iteration of the exponential weighted moving average model. The proposed method is verified using the SCADA monitoring data of wind turbines in two wind farms. The results show that this method can effectively mine the internal correlation coupling information of SCADA multivariable time series, which can effectively improve the accuracy of wind turbine anomaly detection and the robustness to environmental interferences.