•  
  •  
 

Keywords

switchgear; partial discharge; integrated sensor; multimodal fusion; detection and localization

Abstract

To achieve efficient classification and three-dimensional (3D) localization of partial discharge (PD) in switchgear, an integrated circular array sensor combining three types of signals, namely transient earth voltage (TEV), ultra-high frequency (UHF), and acoustic array (AA), is designed. A multimodal fusion classification model and an improved nonlinear crested porcupine optimizer (CPO) algorithm are proposed. First, a sensor system with multi-channel acquisition capability is constructed through structural integration and performance testing. Second, a neural network model is built based on nine-dimensional time-frequency domain features to achieve accurate classification of multiple types of discharge defects. Finally, a time delay extraction method combining ∣Δ∣-value time difference screening and a cross-correlation algorithm is proposed, and a consistency objective function with physical constraints is constructed. The CPO algorithm is improved to enhance the robustness and stability of localization. Experimental results indicate that the defect classification accuracy reaches 91.38%, and the localization error is controlled at the 10 mm level, validating the effectiveness of the method in terms of classification capability and localization performance. This study provides a new technical solution for the detection and localization of partial discharge in electrical equipment.

DOI

10.19781/j.issn.1673-9140.2026.01.030

First Page

319

Last Page

330

Download

Share

COinS