浙江电力2025，Vol.44Issue(2)：124-132,9.DOI:10.19585/j.zjdl.202502012
基于余弦相似度的GIS放电谱图识别精度提高方法
A cosine similarity-based method for improving the accuracy of GIS discharge spec-trum recognition
摘要
Abstract
The study of effective feature extraction and optimization algorithms is crucial for improving the accuracy of partial discharge(PD)detection in gas-insulated switchgear(GIS).To enhance the precision and reliability of GIS partial discharge detection by utilizing the phase information in discharge spectra,this paper proposes a neural network-based PD pattern recognition method that incorporates cosine similarity.By analyzing the spectra of various discharge types,phase features are summarized,and the features of each discharge type are compared with the spectrum of the discharge under evaluation.The resulting phase reference values are incorporated into the network structure for training.The research findings demonstrate that introducing cosine similarity calculations enhances the neural network's accuracy by 3.9%,and that the improvement is closely related to the spectral phase features—greater phase feature clarity leads to more substantial accuracy gains from the cosine similarity module.This method significantly improves PD fault recognition accuracy,equipping GIS with enhanced warning and fault evaluation ca-pabilities.关键词
气体绝缘封闭组合电器/局部放电/神经网络/余弦相似度计算/放电相位谱图Key words
GIS/PD/neural network/cosine similarity calculation/discharge phase spectrum引用本文复制引用
陈孝信,周童浩,刘江明,戴鹏飞,刘延琦,李文栋,张冠军..基于余弦相似度的GIS放电谱图识别精度提高方法[J].浙江电力,2025,44(2):124-132,9.基金项目
国网浙江省电力有限公司科技项目(B311DS23000K) （B311DS23000K）
