Online Robust Subspace Clustering With Application to Power Grid Monitoring
In this work, a robust subspace clustering algorithm is developed to exploit the inherent union-of-subspaces structure in the data for reconstructing missing measurements and detecting anomalies. Our focus is on processing an incessant stream of large-scale data such as synchronized phasor measureme...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10070758/ |
| _version_ | 1797860421089099776 |
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| author | Young-Hwan Lee Seung-Jun Kim Kwang Y. Lee Taesik Nam |
| author_facet | Young-Hwan Lee Seung-Jun Kim Kwang Y. Lee Taesik Nam |
| author_sort | Young-Hwan Lee |
| collection | DOAJ |
| description | In this work, a robust subspace clustering algorithm is developed to exploit the inherent union-of-subspaces structure in the data for reconstructing missing measurements and detecting anomalies. Our focus is on processing an incessant stream of large-scale data such as synchronized phasor measurements in the power grid, which is challenging due to computational complexity, memory requirement, and missing and corrupt observations. In order to mitigate these issues, a low-rank representation (LRR) model-based subspace clustering problem is formulated that can handle missing measurements and sparse outliers in the data. Then, an efficient online algorithm is derived based on stochastic approximation. The convergence property of the algorithm is established. Strategies to maintain a representative yet compact dictionary for capturing the subspace structure are also proposed. The developed method is tested on both simulated and real phasor measurement unit (PMU) data to verify the effectiveness and is shown to significantly outperform existing algorithms based on simple low-rank structure of data. |
| first_indexed | 2024-04-09T21:45:34Z |
| format | Article |
| id | doaj.art-652796bb11a646b5bc6537d30e67dbd1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T21:45:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-652796bb11a646b5bc6537d30e67dbd12023-03-24T23:00:39ZengIEEEIEEE Access2169-35362023-01-0111278162782810.1109/ACCESS.2023.325735710070758Online Robust Subspace Clustering With Application to Power Grid MonitoringYoung-Hwan Lee0Seung-Jun Kim1https://orcid.org/0000-0002-5504-4997Kwang Y. Lee2https://orcid.org/0000-0002-9965-9117Taesik Nam3https://orcid.org/0009-0003-2245-8768Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USADepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USADepartment of Electrical and Computer Engineering, Baylor University, Waco, TX, USAGreen Energy and Nano Technology Research and Development Group, Korea Institute of Industrial Technology, Gwangju, South KoreaIn this work, a robust subspace clustering algorithm is developed to exploit the inherent union-of-subspaces structure in the data for reconstructing missing measurements and detecting anomalies. Our focus is on processing an incessant stream of large-scale data such as synchronized phasor measurements in the power grid, which is challenging due to computational complexity, memory requirement, and missing and corrupt observations. In order to mitigate these issues, a low-rank representation (LRR) model-based subspace clustering problem is formulated that can handle missing measurements and sparse outliers in the data. Then, an efficient online algorithm is derived based on stochastic approximation. The convergence property of the algorithm is established. Strategies to maintain a representative yet compact dictionary for capturing the subspace structure are also proposed. The developed method is tested on both simulated and real phasor measurement unit (PMU) data to verify the effectiveness and is shown to significantly outperform existing algorithms based on simple low-rank structure of data.https://ieeexplore.ieee.org/document/10070758/Anomaly detectionincomplete measurementlow-rank representationonline algorithmsubspace clusteringphasor measurement unit |
| spellingShingle | Young-Hwan Lee Seung-Jun Kim Kwang Y. Lee Taesik Nam Online Robust Subspace Clustering With Application to Power Grid Monitoring IEEE Access Anomaly detection incomplete measurement low-rank representation online algorithm subspace clustering phasor measurement unit |
| title | Online Robust Subspace Clustering With Application to Power Grid Monitoring |
| title_full | Online Robust Subspace Clustering With Application to Power Grid Monitoring |
| title_fullStr | Online Robust Subspace Clustering With Application to Power Grid Monitoring |
| title_full_unstemmed | Online Robust Subspace Clustering With Application to Power Grid Monitoring |
| title_short | Online Robust Subspace Clustering With Application to Power Grid Monitoring |
| title_sort | online robust subspace clustering with application to power grid monitoring |
| topic | Anomaly detection incomplete measurement low-rank representation online algorithm subspace clustering phasor measurement unit |
| url | https://ieeexplore.ieee.org/document/10070758/ |
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