Collaborative Domain Adaptation Network for Partial Discharge Source Localization in Gas-Insulated Switchgear | IEEE Journals & Magazine | IEEE Xplore

Collaborative Domain Adaptation Network for Partial Discharge Source Localization in Gas-Insulated Switchgear


Abstract:

Abnormal discharge in gas-insulated switchgear (GIS) is the key cause of insulation failure, and it is also an external manifestation of insulation defects. Sensitive dis...Show More

Abstract:

Abnormal discharge in gas-insulated switchgear (GIS) is the key cause of insulation failure, and it is also an external manifestation of insulation defects. Sensitive discharge source localization is an important goal of GIS partial discharge (PD) monitoring. However, most existing GIS PD source localization methods rely on time-delay estimation, which not only requires a high-precision synchronous sampling device but also suffers from serious interference. To this end, a collaborative domain adaptation network (CDAN) is proposed for GIS PD source localization. First, a synchronous squeezing wavelet transform (SWT) is introduced to filter out the noise, eliminating the influence of noise on GIS PD source localization. Then, 1-D attention convolutional neural network (1DACNN) is constructed to ensure that discriminative temporal fine-grained information is extracted. Next, a CDAN is proposed to transfer the localization knowledge learned from the simulation to actual GIS and achieve high-precision localization through domain matching and alignment. The results demonstrate that the error of the CDAN proposed is only 19.87 cm, which is considerably better than that from other methods. This work provides a reference solution for the GIS PD source localization.
Article Sequence Number: 3512312
Date of Publication: 10 April 2023

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