内蒙古电力技术2024，Vol.42Issue(6)：48-55,8.DOI:10.19929/j.cnki.nmgdljs.2024.0079
一种面向DGA不平衡数据的变压器缺陷识别方法
A Transformer Defect Recognition Method for DGA Unbalanced Data
摘要
Abstract
In response to the problem of imbalanced transformer defect samples and insufficient applicability of the existing diagnostic methods,a transformer defect recognition method for Dissolved Gas Analysis(DGA)imbalanced data in oil is proposed.Firstly,based on DGA analysis of oil samples sent by some power plants in western region of Inner Mongolia,five types of transformer states are reclassified and the differences are increased by adding state features.Then,input features considering gas content detection,uncoded ratio,and triration are constructed to train and test the intelligent diagnostic model.Finally,the improved dynamic multiple Particle Swarm Optimization(PSO)algorithm is selected to optimize the parameters of the Extreme Learning Machine(ELM)neural network.By analyzing samples of undefined types,the effectiveness of the method is verified.The results show that this method is applicable to DGA imbalanced datasets and can accurately identify the operating status and defects of transformers.关键词
变压器/缺陷识别/油中溶解气体分析/不平衡性/动态多种群粒子群优化算法/极限学习机Key words
transformer/defect recognition/dissolved gas analysis(DGA)/imbalance/dynamic multiple particle swarm optimization/extreme learning machine分类
信息技术与安全科学引用本文复制引用
刘学芳,温欣,李昂,窦冰杰,胡学超,李浩铭..一种面向DGA不平衡数据的变压器缺陷识别方法[J].内蒙古电力技术,2024,42(6):48-55,8.基金项目
内蒙古自治区自然科学基金项目"变压器油复合抗氧化剂的筛选及其电化学检测方法研究"(2021BS05005) （2021BS05005）
内蒙古电力科学研究院青年科技人员支持计划项目"蒙西地区充油变压器运行状态智能监测及预警技术研究"(QK-2024-1-02) （QK-2024-1-02）
