A reliable supply with electric power is vital for our society. Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article we propose a combination of network science metrics and machine learning models to predict the risk of desynchronisation events. Network science provides metrics for essential properties of transmission lines such as their redundancy or centrality. Machine learning models perform inherent feature selection and thus reveal key factors that determine network robustness and vulnerability. As a case study, we train and test such models on simulated data from several synthetic test grids. We find that the integrated models are capable of predicting desynchronisation events after line failures with an average precision greater than $0.996$ when averaging over all data sets. Learning transfer between different data sets is generally possible, at a slight loss of prediction performance. Our results suggest that power grid desynchronisation is essentially governed by only a few network metrics that quantify the networks ability to reroute flow without creating exceedingly high static line loadings.
翻译:可靠的电力供应对我们的社会至关重要。 输电线路故障是电网稳定性的最大威胁之一,因为它们可能导致电网分裂成互不同步的碎片。 需要新的概念方法来评估系统稳定性,以补充现有的模拟模型。 在本篇文章中,我们提议将网络科学指标和机器学习模型结合起来,以预测消化事件的风险。 网络科学为输电线路的基本特性提供了指标,如冗余或中心作用。 机器学习模型具有固有的特征选择,从而揭示了决定网络稳健性和脆弱性的关键因素。 作为案例研究,我们培训和测试了从几个合成测试网中模拟数据中的模型。我们发现,综合模型能够预测线性故障后的消化事件,平均精确度高于0.996美元,而平均高于所有数据集的精确度。 学习不同数据集之间的转移一般是可能的,只是略微丧失预测性性能。 我们的结果表明,电网格衰减基本上受少数网络指标的制约,这些指标可以量化网络在不造成极高的静电线装载的情况下进行回流的能力。