Artificial intelligence and physics-based anomaly detection in the smart grid: A survey
Open accessAbstract
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.
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Existing surveys primarily organize the literature along algorithmic, pipeline-oriented, or domain-specific dimensions and do not explicitly structure the ICS/CPS anomaly detection landscape according to the type of data used for detection.
“However, they primarily organize the literature along algorithmic, pipeline-oriented, or domain-specific dimensions and do not explicitly structure the ICS/CPS anomaly detection landscape according to the type of data used for detection.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [6]
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References 57 · Cited by 67
- Authors
- Gaggero, G. B., Girdinio, P., & Marchese, M.
- Affiliations
- University of Genoa (IT)
- Venue
- IEEE Access
- Published
- 2025
- Volume
- 13
- Pages
- 23597-23606
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4406983072
- Cited by
- 67
- References
- 57
- Citations by year
- 2026: 35 · 2025: 32
- FWCI
- 24.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion, other-oa)
- Funders
- Project SEcurity and RIghts In the CyberSpace through the Ministero dell’Università e della Ricerca National Recovery and Resilience Plan, European Union-NextGenerationEU (PE00000014)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electricity Theft Detection Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Keywords
- anomaly detection, smart grid, physics-based modeling, artificial intelligence, machine learning, cyberattacks, equipment failure, power system security, distributed energy resources
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