Identification of Partial Discharge Based on Composite Optical Detection and Transformer-Based Deep Learning Model

Partial discharge (PD) is widely recognized as the early sign of fatal insulation failure of high-voltage (HV) power equipment and is considered a major factor in accelerating insulation degradation. However, the detection of weak PD signals in noisy environments and the identification of PD modes based on poor-quality data are rather challenging. In this work, using compact and implantable optical detection tools, optical emission spectroscopy (OES) and phase-resolved (PR) ultraviolet (UV) are combined to monitor PD. Based on the obtained dataset, a Transformer-based deep learning model is built and its recognition performance is optimized to realize the fast recognition and classification of spark, corona, and surface flashover, with a precision of 100% and an accuracy of 99%. This capability will help to alarm potential HV equipment failure and assess the risk in advance.