Partial Discharge Identification Based on Unsupervised Representation Learning Under Repetitive Impulse Excitation With Ultra-Fast Slew-Rate

Power electronic devices are widely used in renewable power systems, and the characteristics of partial discharge (PD) signals under repetitive impulse excitation are vastly different from those observed under power frequency excitation, causing many pattern recognition methods to fail. This paper proposes a improved identification method based on representation learning that can directly process PD sequences received by ultra-high frequency (UHF) sensors. First, a representation learning architecture based on the Transformer Encoder module is proposed and the model is pretrained in an unsupervised manner to extract features from the raw input discharge sequences. Then, the extracted features are processed using the ridge regression classifier to achieve end-to-end PD source identification. Finally, the effectiveness of the proposed model is validated through an experimental study, achieving an improved accuracy of 98.6%, which is better than classical PD identification deep learning models under repetitive impulse voltage. Furthermore, the model exhibits strong robustness against noise interference and sampling rates. The effectiveness of the model was also tested on an inverter-fed motor stator device. To further validate its practical applicability, it is essential to collect diverse PD data from various electrical equipment.