计算机应用与软件2024，Vol.41Issue(8)：175-181,195,8.DOI:10.3969/j.issn.1000-386x.2024.08.025
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
摘要
Abstract
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Key words
CEEMD/MPE/SDAE/Partial discharge/Feature extraction/Pattern recognition分类
计算机与自动化引用本文复制引用
蒋伟,赵显阳,樊汝森,徐鹏,沈道义,杨俊杰..基于CEEMD-MPE与SDAE的局部放电模式识别[J].计算机应用与软件,2024,41(8):175-181,195,8.基金项目
国家自然科学基金项目(61401269,61572311) （61401269,61572311）
上海市科技创新行动计划地方院校能力建设项目(17020500900) （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) （17SG51）
上海市科委地方院校能力建设项目(20020500700). （20020500700）
