华侨大学学报（自然科学版）2026，Vol.47Issue(1)：41-49,9.DOI:10.11830/ISSN.1000-5013.202507040
基于特高频信号的GIS局部放电诊断方法
Diagnostic Methods for GIS Partial Discharge Based on Ultra-High Frequency Signals
摘要
Abstract
To improve the discriminability of feature extraction and the accuracy of recognition in ultra-high frequency signal-based partial discharge diagnosis for gas insulated switchgear,a partial discharge diagnosis method for gas insulated switchgear is proposed by integrating improved complete ensemble empirical mode de-composition with a Transformer model.First,the improved complete ensemble empirical mode decomposition is used to decompose ultra-high frequency partial discharge signals,where an animated oat optimization algo-rithm is introduced to optimize key parameters and improve decomposition performance.Second,the Pearson correlation coefficient is adopted to further screen the decomposition results,and key feature indicators are ex-tracted to construct the shallow feature matrix of partial discharge signals.Then,a Transformer model im-proved by the multi-head causal self-attention mechanism is introduced to extract and fuse deep features of par-tial discharge signals.Finally,a gas insulated switchgear partial discharge fault simulation platform is built to simulate four typical fault types,and the collected ultra-high frequency partial discharge signals are subjected to feature extraction and fusion using the proposed method.A radial basis function support vector machine model optimized by the animated oat optimization algorithm is then constructed for fault diagnosis.Experimen-tal results show that the proposed method can effectively extract features from ultrahigh frequency partial dis-charge signals and improve the diagnosis accuracy.关键词
特高频/气体绝缘开关设备/局部放电/改进完全集合经验模态分解Key words
ultra-high frequency/gas insulated switchgear/partial discharge/improved complete ensemble empirical mode decomposition分类
信息技术与安全科学引用本文复制引用
张凯祥,方瑞明,尚荣艳,邵鹏飞,彭长青..基于特高频信号的GIS局部放电诊断方法[J].华侨大学学报（自然科学版）,2026,47(1):41-49,9.基金项目
国家自然科学基金资助项目(52477048) （52477048）
福建省高校产学合作项目(2024H6009) （2024H6009）
福建省厦门市自然科学基金资助项目(3502Z202373952) （3502Z202373952）
福建省厦门市高校科研院所产学研项目(2023CXY0201、2024CXY0230) （2023CXY0201、2024CXY0230）
