A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence
The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/3/1194 |
| _version_ | 1827354833173086208 |
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| author | Marcelo Fabian Guato Burgos Jorge Morato Fernanda Paulina Vizcaino Imacaña |
| author_facet | Marcelo Fabian Guato Burgos Jorge Morato Fernanda Paulina Vizcaino Imacaña |
| author_sort | Marcelo Fabian Guato Burgos |
| collection | DOAJ |
| description | The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology. |
| first_indexed | 2024-03-08T04:00:20Z |
| format | Article |
| id | doaj.art-300f87aecf574281acb0b81029326506 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T04:00:20Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-300f87aecf574281acb0b810293265062024-02-09T15:08:12ZengMDPI AGApplied Sciences2076-34172024-01-01143119410.3390/app14031194A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial IntelligenceMarcelo Fabian Guato Burgos0Jorge Morato1Fernanda Paulina Vizcaino Imacaña2Department of Computer Science, Campus Leganés, Universidad Carlos III de Madrid, 28911 Leganés, SpainDepartment of Computer Science, Campus Leganés, Universidad Carlos III de Madrid, 28911 Leganés, SpainSchool of Computer Science, Faculty of Technical Sciences, Main Campus, Universidad Internacional del Ecuador, Quito 170411, EcuadorThe size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology.https://www.mdpi.com/2076-3417/14/3/1194smart gridcyber physical systemsanomaly detectionartificial intelligence |
| spellingShingle | Marcelo Fabian Guato Burgos Jorge Morato Fernanda Paulina Vizcaino Imacaña A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence Applied Sciences smart grid cyber physical systems anomaly detection artificial intelligence |
| title | A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence |
| title_full | A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence |
| title_fullStr | A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence |
| title_full_unstemmed | A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence |
| title_short | A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence |
| title_sort | review of smart grid anomaly detection approaches pertaining to artificial intelligence |
| topic | smart grid cyber physical systems anomaly detection artificial intelligence |
| url | https://www.mdpi.com/2076-3417/14/3/1194 |
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