用智能算法选最少的相量测量单元，让故障定位识别更准更快。
Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification
- 基于SVM排序候选位置，再局部优化找最优部署方案。
- 在IEEE 34/123系统上，故障定位准确率超94%，类型识别超99%。
- 适合电力系统故障诊断研究者，尤其关注传感器布局优化者。
本文提出一种数据驱动的前向选择与邻域精炼（FSNR）算法，用于确定相量测量单元（PMUs）的数量与位置，以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，并结合局部邻域探索对每一步选择进行优化，得到近似最优的传感器集合。该集合随后输入一维卷积神经网络（1D CNN），从时序测量数据中实现故障线路定位与故障类型分类。在改进的IEEE 34-和IEEE 123-母线系统上的评估表明，所提FSNR-SVM方法能识别出最小的PMU配置，实现最佳的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94%，故障类型分类准确率约99.8%。
原文摘要 · Abstract (English)
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.
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