Identification of Abnormal data in online dissolved gas monitoring in oil based on PCA and Fast-MCD

To detect the operational status of transformers by online monitoring of dissolved gases in oil is critical for ensuring its stable operation. However， faults in monitoring devices can result in abnormal data， severely impacting the effect of online monitoring. To address this issue， a method for identifying abnormal data in dissolved gas monitoring based on principal component analysis （PCA） and fast minimum covariance determinant （Fast-MCD） is proposed. First， the PCA is used to reduce the dimensionality of time-series data for dissolved gas in oil， effectively eliminating redundant features. Next， robust statistical theory is applied to the reduced data using Fast-MCD to identify anomalies. Finally， the identified anomalies are analyzed to determine the source of the fault. Case studies demonstrate that the proposed method effectively identifies faults in online dissolved gas monitoring devices， outperforming conventional methods with an accuracy of 99.1%.