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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.02192 |
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| _version_ | 1866909766268223488 |
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| author | Khattak, Khalid Daud Choudhry, Muhammad A. |
| author_facet | Khattak, Khalid Daud Choudhry, Muhammad A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification Khattak, Khalid Daud Choudhry, Muhammad A. Systems and Control Machine Learning 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. |
| title | Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2509.02192 |