A Method for Detecting Anomaly Conditions of Wind Turbines Using Stacked Denoising Autoencoders with Sliding Window and Multiple Noise Ratios
Chen Junsheng1, Li Jian1, Chen Weigen1, Sun Peng2
1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing 400044 China;
2. State Grid Henan Electrical Power Research Institute Zhengzhou 450000 China
This paper presents a multivariable reconstruction approach to detect the anomaly conditions of wind turbines (WTs), focusing on the data collected from the wind farm supervisory control and data acquisition (SCADA) system. Firstly, the stacked denoising autoencoders (SDAE) model with sliding window was developed to capture nonlinear correlations among multiple variables and temporal dependencies at each variable simultaneously, and reconstruct the status data of WTs. Secondly, the SDAE model with sliding window was trained under multiple noise ratios to learn the coarse-grained and fine-grained features of condition parameters for improving the feature representation of model. Finally, the Mahalanobis distance (MD) of reconstruction error was defined as the monitoring index. The threshold of monitoring index was obtained based on the probability density function (PDF) of MD with the kernel density estimation. The duration of over-limit was utilized to detect the anomaly conditions of WTs. The anomaly conditions of WTs were identified based on the contribution of each variable. The analysis results of SCADA data collected from a wind farm in Eastern China show that the proposed method is effective for the anomaly condition detection of actual wind turbines.
陈俊生, 李剑, 陈伟根, 孙鹏. 采用滑动窗口及多重加噪比堆栈降噪自编码的风电机组状态异常检测方法[J]. 电工技术学报, 2020, 35(2): 346-358.
Chen Junsheng, Li Jian, Chen Weigen, Sun Peng. A Method for Detecting Anomaly Conditions of Wind Turbines Using Stacked Denoising Autoencoders with Sliding Window and Multiple Noise Ratios. Transactions of China Electrotechnical Society, 2020, 35(2): 346-358.
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2020, Vol. 35 
