Title: Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification Show Chinese title

Title: 基于CNN的故障定位与识别的最优PMU数量和位置选择

Abstract: In this paper, we present a data-driven Forward Selection with Neighborhood Refinement (FSNR) algorithm to determine the number and placement of Phasor Measurement Units (PMUs) for maximizing deep-learning-based fault diagnosis performance. Candidate PMU locations are ranked via a cross-validated Support Vector Machine (SVM) classifier, and each selection is refined through local neighborhood exploration to produce a near-optimal sensor set. The resulting PMU subset is then supplied to a 1D Convolutional Neural Network (CNN) for faulted-line localization and fault-type classification from time-series measurements. Evaluation on modified IEEE 34- and IEEE 123-bus systems demonstrates that the proposed FSNR-SVM method identifies a minimal PMU configuration that achieves the best overall CNN performance, attaining over 96 percent accuracy in fault location and over 99 percent accuracy in fault-type classification on the IEEE 34 system, and approximately 94 percent accuracy in fault location and around 99.8 percent accuracy in fault-type classification on the IEEE 123 system. Show Chinese abstract

Abstract: 在本文中，我们提出了一种数据驱动的前向选择与邻域优化（FSNR）算法，以确定相量测量单元（PMUs）的数量和位置，从而最大化基于深度学习的故障诊断性能。通过交叉验证的支持向量机（SVM）分类器对候选PMU位置进行排序，并通过局部邻域探索对每次选择进行优化，以生成一个接近最优的传感器集合。然后将得到的PMU子集提供给一维卷积神经网络（CNN），以从时间序列测量中进行故障线路定位和故障类型分类。在修改后的IEEE 34和IEEE 123节点系统上的评估表明，所提出的FSNR-SVM方法识别出一个最小的PMU配置，实现了最佳的整体CNN性能，在IEEE 34系统上故障定位准确率超过96%，故障类型分类准确率超过99%；在IEEE 123系统上，故障定位准确率约为94%，故障类型分类准确率约为99.8%。