南方电网技术2026，Vol.20Issue(2)：66-77,12.DOI:10.13648/j.cnki.issn1674-0629.2026.02.007
基于GSABO-ICEEMDAN-KELM的局部放电识别方法在气体绝缘开关设备故障诊断中的应用
Application of Partial Discharge Identification Method Based on GSABO-ICEEMDAN-KELM in Fault Diagnosis of Gas-Insulated Switchgear Devices
摘要
Abstract
Gas-insulated switchgear(GIS)exhibits various insulation defects during production and operation,and accurately identify-ing partial discharge signals caused by insulation defects is of significant importance for ensuring the safety of GIS equipment and power systems.By integrating the golden sine algorithm(golden-SA)to improve the subtraction-average-based optimizer(SABO),a fused golden sine-improved SABO optimization algorithm(GSABO)is obtained.This algorithm is applied to optimize parameters for the improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and the kernel extreme learn-ing machine(KELM)to achieve recognition of GIS partial discharge faults.Firstly,to address issues such as SABO possibly falling into local optima and insufficient convergence speed,chaotic mapping and the golden sine are introduced to improve it.Then,an experimental platform is set up to collect four types of typical partial discharge signals,which are decomposed using GSABO-ICEEMDAN,and effective modal components are screened using the correlation coefficient method.Finally,the sample entropy of the selected modal components is calculated to form a feature matrix,which is input into GSABO-KELM for fault classification and recognition.Experimental analysis shows that,compared to the unimproved SABO algorithm,GSABO demonstrates significant advantages in escaping local optima,convergence speed,and accuracy.Compared with other traditional algorithms,GSABO-ICEEMDAN-KELM achieves a recognition accuracy of 99.1667%,verifying the accuracy and superiority of this algorithm,which holds reference value for engineering applications in GIS partial discharge fault diagnosis.关键词
气体绝缘组合电器/局部放电/ICEEMDAN/改进减法优化算法/黄金正弦算法/核极限学习机/故障诊断Key words
gas-insulated switchgear/partial discharge/ICEEMDAN/subtraction-average-based optimization algorithm/golden sine algorithm/kernel extreme learning machine/fault diagnosis分类
信息技术与安全科学引用本文复制引用
王思涵,马宏忠,孙维,葛威,陈悦林..基于GSABO-ICEEMDAN-KELM的局部放电识别方法在气体绝缘开关设备故障诊断中的应用[J].南方电网技术,2026,20(2):66-77,12.基金项目
国家自然科学基金资助项目(51577050) （51577050）
国网江苏省电力有限公司科技项目(J2023002). Supported by the National Natural Science Foundation of China(51577050) （J2023002）
the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(J2023002). （J2023002）
