A technique for separating partial discharge pulses of typical defects in cable accessories based on UMAP

To overcome the challenges of high signal-to-noise ratio requirements and the poor applicability of pulse waveform features in partial discharge (PD) blind source separation for cable accessories, this paper proposes a pulse source separation technique based on Uniform Manifold Approximation and Projection (UMAP). This method enables pulse signal separation in the presence of various noise interferences by using the time-frequency spectrogram of PD pulses as input and applying the UMAP algorithm for dimensionality reduction and feature extraction of the spectrogram's global characteristics. Unlike traditional feature extraction methods, this approach eliminates the need for defining and selecting specific features. The reduced-dimensional features effectively capture the relative differences between different discharge pulses, enabling their separation. Experimental validation using data from typical cable accessory defects shows that this method can effectively extract and separate features from random pulses. Compared to similar manifold approximation algorithms, UMAP is less sensitive to hyperparameters, and the reduced-dimensional features are more stable, which facilitates the execution of clustering algorithms.