Cable joint fault detection for the ring main unit based on an adaptive TNPE algorithm

The ring main unit (RMU) is the main equipment of the electrical power distribution network. The deterioration process of the RMU cable joint is multivariable and nonstationary over time. Existing fault detection models for the RMU cable joint are single‐variable and static over time. A novel algorithm of adaptive time neighborhood preserving embedding (ATNPE) is proposed in this paper, a multivariable time series statistical algorithm with online learning ability to improve the robustness and generalization ability of cable joint fault detection. This method combines an approximate linear dependence (ALD) condition with the time neighborhood preserving embedding (TNPE). New independent samples were determined by calculating the ALD value between new samples and modeling samples. In addition, the new independent samples were used to update the TNPE model. The offline training module was used to update RMU fault detection based on the TNPE model by training new samples with historical offline data. The online updating module trained the historical model to ensure the correct rate of online detection. The experimental results indicated that the method has the ability to adapt to new samples and the squared prediction error statistic limit responded to changes in statistical characteristics of monitoring data. The method can effectively reduce the false alarm rate and has the same fault detection rate compared to the existing model.