摘要
为了研究变压器油纸系统匝间局部放电,该研究构建了基于超声信号的检测试验平台,并采用恒压法获取数据。通过设计数据预处理方法、提取超声信号的时域和频域特征,分析特征参量的发展特性。基于发展特性将匝间局部放电划分为3个阶段,起始阶段、发展阶段和预击穿阶段,通过层次聚类算法验证其合理性,并构建了基于斑翠鸟优化算法的最小二乘支持向量机-自适应增强模型的阶段识别方法。实验结果表明,匝间超声信号特征在发展阶段波动较小,起始阶段和预击穿阶段有明显突变;3个阶段的匝间放电数据样本的识别正确率均超过96%,总体识别正确率为98%。
In this study,a test platform for detecting interturn partial discharge(IPD)in transformer oil-paper insulation systems,based on ultrasonic signals,is established.Data acquisition is performed using the constant voltage method.Through designing a data preprocessing method for ultrasonic signals,the time-domain and frequency-domain characteristics of the signals are extracted,and the development characteristics of the characteristic parameters are analyzed.According to these evolutionary patterns,inter-turn partial discharge is categorized into three stages:the initial stage,the development stage,and the pre-breakdown stage.The hierarchical clustering algorithm is employed to validate the rationality of this staging.Additionally,a least squares support vector machine-adaptive boosting model,optimized by the pied kingfisher optimization algorithm,is constructed.Test results indicate that the characteristics of inter-turn ultrasonic signals exhibit minimal fluctuation during the development stage,whereas significant mutations occur in the initial stage and pre-breakdown stage.The recognition accuracy for inter-turn discharge data samples across the three stages exceeds 96%,with an overall recognition accuracy of 98%.
作者
付文光
薛利军
郑璐
戴景琪
董明
焦在滨
王昊
FU Wenguang;XUE Lijun;ZHENG Lu;DAI Jingqi;DONG Ming;JIAO Zaibin;WANG Hao(Inner Mongolia Power Research Institute Branch of Inner Mongolia Power(Group)Co.,Ltd.,Hohhot 010020,Inner Mongolia,China;Key Enterprise Laboratory of Smart Grid for New Power System in Inner Mongolia Autonomous Region,Hohhot 010020,Inner Mongolia,China;Ordos Electric Power Supply Company,Inner Mongolia Power(Group)Co.,Ltd.,Ordos 017000,Inner Mongolia,China;Inner Mongolia Power(Group)Co.,Ltd.,Hohhot 010000,Inner Mongolia,China;Xi’an Jiaotong University,Xi’an 710049,Shaanxi,China)
出处
《电网与清洁能源》
北大核心
2025年第12期85-93,共9页
Power System and Clean Energy
基金
国家重点研究发展计划项目(2023YFB2406905)
内蒙古电力(集团)有限责任公司科技项目(2024-4-53)。
关键词
匝间放电
超声信号
阶段识别
斑翠鸟优化
inter-turn discharge
ultrasonic signal
stage identification
pied kingfisher optimization(PKO)algorithm
