Deep-learning based optimal PMU placement and fault classification for power system
Date
2025
Authors
Lei, X.
Li, Z.
Jiang, H.
Yu, S.S.
Chen, Y.
Liu, B.
Shi, P.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Expert Systems with Applications, 2025; 292:128586-1-128586-11
Statement of Responsibility
Xin Lei, Zhen Li, Huaiguang Jiang, Samson S. Yu, Yu Chen, Bin Liu, Peng Shi
Conference Name
Abstract
Phasor 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.
School/Discipline
Dissertation Note
Provenance
Description
Available online 14 June 2025
Access Status
Rights
© 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.