Deep-learning based optimal PMU placement and fault classification for power system

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dc.contributor.authorLei, X.
dc.contributor.authorLi, Z.
dc.contributor.authorJiang, H.
dc.contributor.authorYu, S.S.
dc.contributor.authorChen, Y.
dc.contributor.authorLiu, B.
dc.contributor.authorShi, P.
dc.date.issued2025
dc.descriptionAvailable online 14 June 2025
dc.description.abstractPhasor measurement units (PMUs) are vital for power grid monitoring, yet their high cost restricts widespread adoption. PMU measurement data is also crucial for fault analysis in power systems. However, existing research seldom explores the interplay between optimal PMU placement (OPP) and fault analysis, impeding advancements in grid economy and security. This study introduces a perception-driven, deep learning-based optimization approach that integrates OPP, multi-task learning, and fault data augmentation. First, deep reinforcement learning optimizes PMU placement, balancing cost-effectiveness with observability requirements. Next, multi-task learning, enhanced by Bayesian optimization, improves fault classification efficiency using PMU data. Finally, pretrained models paired with 𝑘-means clustering augment fault data, boosting classification accuracy. Extensive simulations across four IEEE standard test systems validate the proposed method’s effectiveness.
dc.description.statementofresponsibilityXin Lei, Zhen Li, Huaiguang Jiang, Samson S. Yu, Yu Chen, Bin Liu, Peng Shi
dc.identifier.citationExpert Systems with Applications, 2025; 292:128586-1-128586-11
dc.identifier.doi10.1016/j.eswa.2025.128586
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.orcidShi, P. [0000-0001-6295-0405] [0000-0001-8218-586X] [0000-0002-0864-552X] [0000-0002-1358-2367] [0000-0002-5312-5435]
dc.identifier.urihttps://hdl.handle.net/2440/146083
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/IC210100021
dc.rights© 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
dc.source.urihttps://doi.org/10.1016/j.eswa.2025.128586
dc.subjectData augmentation; Deep reinforcement learning; Multi-task learning; Optimal PMU placement; Phase measurement unit; Power system fault classification
dc.titleDeep-learning based optimal PMU placement and fault classification for power system
dc.typeJournal article
pubs.publication-statusPublished

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