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.

翻译：本研究将多种机器学习算法应用于大规模高压电网运行数据的异常检测问题。我们观察到不同算法在性能上存在显著差异。神经网络通常优于k近邻和支持向量机等经典算法，我们将此归因于异常具有强烈的上下文相关性特征。研究表明，无监督学习算法表现出色，其预测结果对同时发生的并发异常具有鲁棒性。