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    Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

    Jun Zhao1, Shuguo Gao1, Yunpeng Liu2,3, Quan Wang2,*, Ziqiang Xu2, Yuan Tian1, Lu Sun1

    Energy Engineering, Vol.119, No.5, pp. 1811-1828, 2022, DOI:10.32604/ee.2022.020490

    Abstract Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change, external environmental interference, communication interruption, and other factors, a method of anomaly recognition and differentiation for monitoring data was proposed. Firstly, the empirical wavelet transform (EWT) and the autoregressive integrated moving average (ARIMA) model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value, and then the isolation forest algorithm was used to identify the abnormal information, and the monitoring sequence More >

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