Noise Separation and Discriminative Feature Learning for Partial Discharge Recognition

Developing intelligent methods for partial discharge (PD) diagnosis, capable of handling various types of insulation defects in switchgear, has garnered significant attention in recent years. Certain PD signals exhibit similar characteristics, often leading to their confusion with noisy signals during data acquisition. To mitigate noise interference and enhance the precision of PD recognition, this article introduces a novel framework for separating PD signals from noise and acquiring discriminative features for identifying different types of PDs. Specifically, the proposed approach incorporates an adaptive frequency sampling strategy to extract effective and efficient features for the separation of PD signals and noise, followed by the clustering of the captured signals. Phase Resolved PD (PRPD) patterns are then generated for each clustered signal group, forming the PRPD pattern database. In order to identify the informative region within the PRPD patterns, we introduce spatial correlation attention and discriminative feature learning modules. These modules aim to reduce intraclass variance and increase interclass differences in the PRPD patterns. To evaluate the effectiveness of the proposed method in separating PD signals from noise and recognizing different PD patterns, we constructed a PD recognition dataset that encompasses noise as well as three types of PDs: 1) corona, 2) internal, and 3) surface. By conducting experiments and comparing the results with state-of-the-art methods, we demonstrate the performance of our method in achieving accurate PD recognition with a notable improvement of 1.9% on the constructed PD dataset.