Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids
This work addresses anomaly detection for power grid operators, but it is incremental as it applies existing methods to a specific domain without introducing new techniques.
The study applied various machine learning algorithms to detect anomalies in large-scale power grid operational data, finding that neural networks generally outperformed classical methods like k-nearest neighbors and support vector machines, with unsupervised learning showing robust performance against multiple anomalies.
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.