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Cleaning Method for Status Monitoring Data of Power Equipment Based on Stacked Denoising Autoencoders

Currently, the cleaning process for power equipment monitoring data is cumbersome and often leads to loss of information. To address these problems, a data cleaning method based on stacked denoising autoencoder (SDAE) networks is proposed in this paper. SDAE networks have a strong ability to denoise and restore corrupted data and have a strong feature extraction capability. The status monitoring data of equipment under normal conditions are trained by SDAE to obtain the cleaning parameters and the reconstruction errors. An upper threshold of the reconstruction errors obtained from training samples is determined through Kernel density estimation. A tolerance window width is added to achieve rapid anomaly detection. The abnormal data are classified as outliers, missing data, or fault status data according to the relationship between the reconstruction error and the threshold and between the duration of abnormal data and the tolerance window. To verify the effectiveness of the proposed method, the SDAE model is used to process the data for the dissolved gas concentration in transformer oil and the temperature of the transmission line. The results show that the proposed method can effectively identify and repair outliers and missing information. The model can perform rapid anomaly detection when the equipment is running abnormally.