Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification

• URL: http://arxiv.org/abs/2509.02192v1
• Date: Tue, 02 Sep 2025 11:05:58 GMT
• Title: Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification
• Authors: Khalid Daud Khattak, Muhammad A. Choudhry,
• Abstract summary: We present a data-driven Forward Selection with Neighborhood Refinement (FSNR) algorithm to determine the number and placement of Phasor Measurement Units (PMUs)<n>The proposed FSNR-SVM method identifies a minimal PMU configuration that achieves the best overall CNN performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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