电力电缆故障放电声自动识别与起点标定算法  

Algorithm of Power Cable Fault Discharge Sound Automatic Recognizing and Initial Point Marking

作　　者：刘一铭 李峰 徐丙垠[1,2] LIU Yiming;LI Feng;XU Bingyin(Shandong University of Technology,School of Electrical and Electronic Engineering,Zibo,Shandong 255049,China;Shandong Kehui Power Automation Co.,Ltd.,Zibo,Shandong 255000,China)

机构地区：[1]山东理工大学电气与电子工程学院,山东淄博255049 [2]山东科汇电力自动化有限公司,山东淄博255000

出　　处：《南方电网技术》2020年第3期35-41,共7页Southern Power System Technology

摘　　要：针对电力电缆故障精确定点方法中存在的依赖人工判断、效率低下的缺点,提出了一种电缆故障放电声波自动识别及波形起点标定算法。通过对电缆故障放电声波进行特征分析,定义了4个概括性特征,对大量故障、非故障波形进行了特征提取并组建了训练、验证样本集;提出了基于AdaBoost-SVM(支持向量机)的故障放电声波识别算法,对所提出的4个特征在放电和噪声信号中的空间分布差异进行了学习;结合离散小波变换和高斯分布规律提出了故障波形起点自动标定算法。实验证明,所提算法在保证准确性的同时,可提升电缆故障精确定点的效率。Aiming at the drawbacks of manual judgement reliance and low efficiency in power cable fault locating technologies,a novel algorithm using fault soundwave auto-recognition and initial point auto-marking is proposed.Four general features are defined by analyzing the fault soundwave characteristics,by performing feature extractions on fault and non-fault soundwaves,training set and test set are forged.An AdaBoost-support vector machine(SVM)based algorithm for fault soundwave recognition is proposed,which is used to train the four proposed features in spatial distributing difference of discharge and noise signals.A fault soundwave initial point marking algorithm is proposed by combining discrete wavelet transformation and Gaussian distribution regularities.Experiment verifies that the proposed algorithms increase its efficiency in fault pinpointing while maintaining the accuracy.

关 键 词：电力电缆 故障定点 支持向量机 ADABOOST 信号识别 

分 类 号：TM247[一般工业技术—材料科学与工程]