基于无监督学习的交叉互联电缆行波测距方法

为提高交叉互联电缆故障测距精度，提出了一种基于无监督学习的交叉互联电缆行波测距新方法。首先结合直埋敷设方式的实际工程背景分析了电缆各个电流模量之间的关系，给出了采取三相护层电流之和作为故障测距信号的依据。其次，通过无监督学习的主成分分析法对由直接接地箱和交叉互联箱中采集到数据组成的高维矩阵降维，采用密度聚类算法对降维后的样本进行聚类。最后选取含样本量最少的簇类所匹配的区段为故障区段并用双端测距公式测距。PSCAD仿真结果表明，所提方法可避免交叉互联点对护层电流行波的影响，使得故障测距在不同故障距离下仍有较高的准确度和可靠性，且不受过渡电阻的影响。

Traveling Wave Ranging Method for Cross-bonded Cable Based on Unsupervised Learning

A new traveling wave ranging method of the cross-bonded cable based on unsupervised learning is proposed to improve the fault location accuracy of cross-bonded cables. Firstly，the relationship among the current modes is analyzed in combination with the direct buried laying method of actual engineering environment. The algorithm gives the basis of taking the sum of the three-phase sheath current as the fault location signal. Secondly，the principal component analysis method of unsupervised learning is used to reduce the dimensionality of the high-dimensional matrix composed of the data collected from the direct grounding box and the cross-connecting box， and the density clustering algorithm is used to cluster the samples after dimensionality reduction.Finally，the segment by matching the cluster class with the smallest sample size is selected as fault segment， and double end ranging formula is used to measure the distance. The simulation results of PSCAD show that the proposed method can avoid the influence of cross interconnection points on the electrical wave propagation of the sheath， so that the fault location can still have high accuracy and reliability at different fault distances， and is not affected by the transition resistance.