基于多尺度注意力自适应去噪网络的局部放电模式识别  

Partial Discharge Pattern Recognition Based on Multi-scale Attention Adaptive Denoising Network

作　　者：郑尚坡 刘俊峰[1] 曾君[2] 廖晓青 陈历 许建远 ZHENG Shangpo;LIU Junfeng;ZENG Jun;LIAO Xiaoqing;CHEN Li;XU Jianyuan(School of Automation Science and Engineering,South China University of Technology,Guangzhou 510641,China;School of Electric Power Enineering,South China University of Technology,Guangzhou 510641,China;Maoming Power Supply Bureau,Guangdong Power Grid Co.,Ltd.,Maoming 525000,China)

机构地区：[1]华南理工大学自动化科学与工程学院,广州510641 [2]华南理工大学电力学院,广州510641 [3]广东电网有限责任公司茂名供电局,茂名525000

出　　处：《高电压技术》2025年第4期1958-1968,共11页High Voltage Engineering

基　　金：中国南方电网公司科技项目(030900KC23040006)。

摘　　要：局部放电的故障类型与气体绝缘开关(gas insulated switchgear,GIS)绝缘故障的严重程度紧密相关,精确识别局部放电故障类型对保障供电系统的稳定性至关重要。传统局部放电模式识别方法缺乏自适应去噪和对多尺度故障特征的处理机制,并且过于依赖专家经验,以至于在处理含有大量噪声和固有多尺度特性的复杂局部放电信号时存在显著局限性,从而限制了模型对于局部放电故障识别准确率的进一步提升。为解决这些问题,提出一种多尺度注意力自适应去噪网络(multi-scale attention adaptive denoising network,MAADNet),该网络集成了多尺度特征学习模块、卷积注意力模块(convolutional block attention module,CBAM)以及软阈值函数,具备强大的自适应去噪和多尺度故障特征学习能力。具体而言,多尺度特征学习模块通过采用不同空洞率的空洞卷积以提取多尺度特征;而CBAM注意力机制和软阈值函数协同工作,依据输入局部放电信号的特性自适应调整去噪阈值,有效实现噪声抑制。此外,为验证所提网络有效性,通过搭建局部放电试验平台,设计并制作4种典型局放故障模型以收集不同故障类型的局部放电数据集。试验结果表明,所提方法在局部放电数据集上取得94.34%的识别准确率,优于其他先进方法,显示出良好的应用前景。The types of partial discharge faults are closely related to the severity of insulation failures in gas insulated switchgear(GIS),thus the precise identification of these faults is crucial for maintaining the stability of power supply systems.Traditional methods of partial discharge pattern recognition lack adaptive denoising capabilities and mechanisms to handle multi-scale fault features,and they primarily rely on expert experience.This results in significant limitations when dealing with complex partial discharge signals that contain substantial noise and inherent multi-scale characteristics,thereby constraining further improvements in the accuracy of fault type recognition.To address these challenges,this paper introduces a multi-scale attention adaptive denoising network(MAADNet),which integrates a multi-scale feature learning module,a convolutional block attention module(CBAM)attention mechanism module,and a soft threshold function.This network possesses robust adaptive denoising and multi-scale fault feature learning capabilities.Specifically,the multi-scale feature learning module employs dilated convolutions with varying dilation rates to extract features at multiple scales;the CBAM attention mechanism and soft threshold function collaboratively adjust the denoising threshold based on the characteristics of the input partial discharge signals,effectively suppressing noise.Moreover,to validate the effectiveness of the proposed network,a partial discharge experimental platform was set up,and four typical partial discharge fault models were designed and constructed to collect a dataset of different fault types.Experimental results demonstrate that the proposed method achieves a recognition accuracy of 94.34%on the partial discharge dataset,outperforming other advanced methods and showing promising application prospects.

关 键 词：气体绝缘开关 局部放电 模式识别 多尺度特征 注意力机制 

分 类 号：TM855[电气工程—高电压与绝缘技术]