Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915792365289472 |
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| author | Gillioz, Marc Dubuis, Guillaume Voutaz, Étienne Jacquod, Philippe |
| author_facet | Gillioz, Marc Dubuis, Guillaume Voutaz, Étienne Jacquod, Philippe |
| contents | We apply several machine learning algorithms to the problem of anomaly detection in operational data for large-scale, high-voltage electric power grids. We observe important differences in the performance of the algorithms. Neural networks typically outperform classical algorithms such as k-nearest neighbors and support vector machines, which we explain by the strong contextual nature of the anomalies. We show that unsupervised learning algorithm work remarkably well and that their predictions are robust against simultaneous, concurring anomalies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10888 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids Gillioz, Marc Dubuis, Guillaume Voutaz, Étienne Jacquod, Philippe Systems and Control Machine Learning We apply several machine learning algorithms to the problem of anomaly detection in operational data for large-scale, high-voltage electric power grids. We observe important differences in the performance of the algorithms. Neural networks typically outperform classical algorithms such as k-nearest neighbors and support vector machines, which we explain by the strong contextual nature of the anomalies. We show that unsupervised learning algorithm work remarkably well and that their predictions are robust against simultaneous, concurring anomalies. |
| title | Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2602.10888 |