Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey
The integration of advanced communication systems and distributed resources has transformed power systems, enhancing control and efficiency in the Smart Grid. However, this increased complexity introduces new vulnerabilities, heightening risks of cyber-attacks, equipment failures, and other anomalie...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858740/ |
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| author | Giovanni Battista Gaggero Paola Girdinio Mario Marchese |
| author_facet | Giovanni Battista Gaggero Paola Girdinio Mario Marchese |
| author_sort | Giovanni Battista Gaggero |
| collection | DOAJ |
| description | The integration of advanced communication systems and distributed resources has transformed power systems, enhancing control and efficiency in the Smart Grid. However, this increased complexity introduces new vulnerabilities, heightening risks of cyber-attacks, equipment failures, and other anomalies. Anomaly detection methods increasingly rely on Machine Learning techniques, that represent a game-changer tool for data analysis. The aim of this survey is to review anomaly detection techniques in the Smart Grid, focusing on methods that combine Artificial Intelligence and physics-based modeling. This work systematically examines the current state of research, evaluating the investigated use cases, the algorithms, the performances and the validation of the papers, identifying key gaps, and offering insights for advancing in this research field. |
| format | Article |
| id | doaj-art-9e7ad31a4c1a411f8ee0b47167d4c465 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9e7ad31a4c1a411f8ee0b47167d4c4652025-02-11T00:01:28ZengIEEEIEEE Access2169-35362025-01-0113235972360610.1109/ACCESS.2025.353741010858740Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A SurveyGiovanni Battista Gaggero0https://orcid.org/0000-0001-6404-2451Paola Girdinio1Mario Marchese2https://orcid.org/0000-0002-9626-3483Department of Electrical, Electronics, and Telecommunications Engineering and Naval Architecture—DITEN, University of Genoa, Genoa, ItalyDepartment of Electrical, Electronics, and Telecommunications Engineering and Naval Architecture—DITEN, University of Genoa, Genoa, ItalyDepartment of Electrical, Electronics, and Telecommunications Engineering and Naval Architecture—DITEN, University of Genoa, Genoa, ItalyThe integration of advanced communication systems and distributed resources has transformed power systems, enhancing control and efficiency in the Smart Grid. However, this increased complexity introduces new vulnerabilities, heightening risks of cyber-attacks, equipment failures, and other anomalies. Anomaly detection methods increasingly rely on Machine Learning techniques, that represent a game-changer tool for data analysis. The aim of this survey is to review anomaly detection techniques in the Smart Grid, focusing on methods that combine Artificial Intelligence and physics-based modeling. This work systematically examines the current state of research, evaluating the investigated use cases, the algorithms, the performances and the validation of the papers, identifying key gaps, and offering insights for advancing in this research field.https://ieeexplore.ieee.org/document/10858740/ReviewAIsmart gridanomaly detectionphysics-based anomaly detection |
| spellingShingle | Giovanni Battista Gaggero Paola Girdinio Mario Marchese Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey IEEE Access Review AI smart grid anomaly detection physics-based anomaly detection |
| title | Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey |
| title_full | Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey |
| title_fullStr | Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey |
| title_full_unstemmed | Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey |
| title_short | Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey |
| title_sort | artificial intelligence and physics based anomaly detection in the smart grid a survey |
| topic | Review AI smart grid anomaly detection physics-based anomaly detection |
| url | https://ieeexplore.ieee.org/document/10858740/ |
| work_keys_str_mv | AT giovannibattistagaggero artificialintelligenceandphysicsbasedanomalydetectioninthesmartgridasurvey AT paolagirdinio artificialintelligenceandphysicsbasedanomalydetectioninthesmartgridasurvey AT mariomarchese artificialintelligenceandphysicsbasedanomalydetectioninthesmartgridasurvey |