基于CEEMD-MPE与SDAE的局部放电模式识别  

PARTIAL DISCHARGE PATTERN RECOGNITION BASED ON CEEMD-MPE AND SDAE

作　　者：蒋伟 赵显阳 樊汝森 徐鹏 沈道义 杨俊杰 Jiang Wei;Zhao Xianyang;Fan Rusen;Xu Peng;Shen Daoyi;Yang Junjie(School of Electronics and Information Engineering,Shanghai University of Electric Power,Shanghai 201306,China;State Grid Shanghai Qingpu Power Supply Company,Shanghai 201700,China;State Grid Shanghai Electric Power Company Electric Power Research Institute,Shanghai 200437,China;Global Infocom Science&Technology(Shanghai)Co.,Ltd.,Shanghai 201210,China;Shanghai Dianji University,Shanghai 201306,China)

机构地区：[1]上海电力大学电子与信息工程学院,上海201306 [2]国网上海市电力公司青浦供电公司,上海201700 [3]国网上海市电力公司电力科学研究院,上海200437 [4]上海格鲁布科技有限公司,上海201210 [5]上海电机学院,上海201306

出　　处：《计算机应用与软件》2024年第8期175-181,195,共8页Computer Applications and Software

基　　金：国家自然科学基金项目(61401269,61572311);上海市科技创新行动计划地方院校能力建设项目(17020500900);上海市教育发展基金会和上海市教育委员会“曙光计划”项目(17SG51);上海市科委地方院校能力建设项目(20020500700)。

摘　　要：针对变压器局部放电故障信息提取困难以及局部放电类型识别准确率低等问题,提出一种基于CEEMD-MPE与SDAE相结合的局部放电模式识别算法。对局部放电原始信号进行CEEMD分解,得到多个固有模态分量(IMF),根据相关系数筛选出系数最大的IMF作为最优分量,计算其不同尺度下的排列熵值;将有效排列熵值作为特征数据集输入到SDAE中进行无监督学习训练;利用Softmax分类器输出放电类型。实验结果表明,该算法识别精准率为98%,召回率为96.67%,F1得分为97.17%,能够快速、准确地识别局部放电类型。Aimed at the difficulties in extracting partial discharge fault information of transformers and low recognition rate of discharge types,a partial discharge pattern recognition method based on complementary ensemble empirical mode decomposition-multiscale permutation entropy(CEEMD-MPE)and stacked denoise auto-encoder(SDAE)is proposed.The CEEMD algorithm was used to decompose the original signals of partial discharge to obtain intrinsic mode functions(IMFs).According to the correlation coefficient,the IMF with the largest correlation coefficient was selected as the optimal component,and the permutation entropy(PE)value under different scales was calculated.The effective PE value was input as the feature dataset into SDAE for unsupervised learning.We use the Softmax classifier to output the discharge.The experimental results show that the algorithm recognition accuracy rate,recall rata and F1 score are 98%,96.67% and 97.17% respectively,which can quickly and accurately recognize partial discharge types.

关 键 词：互补集合经验模态分解 多尺度排列熵 栈式降噪自编码 局部放电 特征提取 模式识别 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术] TM855[电气工程—高电压与绝缘技术]