An Intelligent Suppression Method for Interference Pulses in Partial Discharge Detection of Transformers Based on Waveform Feature Recognition

High-frequency current detection of partial discharge (PD) at transformers on-site faces complex noise interference, which severely impacts the accuracy of PD detection. To address this issue, an intelligent interference suppression algorithm for PD signals based on adaptive waveform feature recognition is proposed. First, a 10 MHz high-pass filter is applied to eliminate the influence of periodic narrowband interference on the zero-crossing count of the time-series. Non-pulse noise is removed based on the instantaneous zero-crossing density of the signal. Next, the start and end times of each pulse are determined, and the corresponding waveform segments are extracted from the original signal to form a pulse array. Subsequently, waveform features of the pulses are extracted, and discrimination thresholds for the feature parameters are calculated based on univariate analysis. Finally, each pulse is adaptively identified based on its waveform features, and PD signals are screened out. The proposed algorithm was tested using PD signals superimposed with on-site noise as well as field-measured signals. The results demonstrate that the algorithm can intelligently identify PD signals and significantly reduce PD signal attenuation, exhibiting excellent suppression effects on complex noise interference in on-site PD detection at transformers.