Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification
This addresses the challenge of efficient sensor deployment for fault diagnosis in power grids, offering a data-driven method to reduce costs while maintaining high accuracy, though it is incremental as it builds on existing deep learning and optimization techniques.
The paper tackles the problem of selecting the optimal number and placement of Phasor Measurement Units (PMUs) to enhance deep-learning-based fault diagnosis in power systems, achieving over 96% accuracy in fault location and over 99% accuracy in fault-type classification on an IEEE 34 system, and approximately 94% and 99.8% accuracy on an IEEE 123 system.
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.