Anomaly Detection in Power System State Estimation: Review and New Directions
Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/18/6678 |
| _version_ | 1797580291156475904 |
|---|---|
| author | Austin Cooper Arturo Bretas Sean Meyn |
| author_facet | Austin Cooper Arturo Bretas Sean Meyn |
| author_sort | Austin Cooper |
| collection | DOAJ |
| description | Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid. |
| first_indexed | 2024-03-10T22:48:57Z |
| format | Article |
| id | doaj.art-25c53d78d55e48ee85aa017923e06f3b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T22:48:57Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-25c53d78d55e48ee85aa017923e06f3b2023-11-19T10:28:32ZengMDPI AGEnergies1996-10732023-09-011618667810.3390/en16186678Anomaly Detection in Power System State Estimation: Review and New DirectionsAustin Cooper0Arturo Bretas1Sean Meyn2Electrical and Computer Engineering Department, University of Florida, Gainesville, FL 32603, USADistributed Systems Group, Pacific Northwest National Laboratory, Richland, WA 99354, USAElectrical and Computer Engineering Department, University of Florida, Gainesville, FL 32603, USAFoundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid.https://www.mdpi.com/1996-1073/16/18/6678anomaly detectioncyber-securityfalse data injectionhypothesis testingmachine learningpower system monitoring |
| spellingShingle | Austin Cooper Arturo Bretas Sean Meyn Anomaly Detection in Power System State Estimation: Review and New Directions Energies anomaly detection cyber-security false data injection hypothesis testing machine learning power system monitoring |
| title | Anomaly Detection in Power System State Estimation: Review and New Directions |
| title_full | Anomaly Detection in Power System State Estimation: Review and New Directions |
| title_fullStr | Anomaly Detection in Power System State Estimation: Review and New Directions |
| title_full_unstemmed | Anomaly Detection in Power System State Estimation: Review and New Directions |
| title_short | Anomaly Detection in Power System State Estimation: Review and New Directions |
| title_sort | anomaly detection in power system state estimation review and new directions |
| topic | anomaly detection cyber-security false data injection hypothesis testing machine learning power system monitoring |
| url | https://www.mdpi.com/1996-1073/16/18/6678 |
| work_keys_str_mv | AT austincooper anomalydetectioninpowersystemstateestimationreviewandnewdirections AT arturobretas anomalydetectioninpowersystemstateestimationreviewandnewdirections AT seanmeyn anomalydetectioninpowersystemstateestimationreviewandnewdirections |