用无监督学习检测电网异常，抗多重并发故障能力强。
Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids
- 采用无监督学习方法检测电网运行数据中的异常
- 神经网络优于传统算法，因异常具强上下文依赖性
- 可有效应对多个异常同时发生的情况，适合电力系统监控
我们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。观察到不同算法表现差异显著：神经网络通常优于k近邻和支持向量机等经典算法，这归因于异常的强上下文特性。研究表明，无监督学习算法表现优异，且其预测对同时发生的多重异常具有鲁棒性。该研究验证了在复杂电网场景下，无监督方法在异常检测中的有效性与稳定性。
原文摘要 · Abstract (English)
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.
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