Predicting Failure Cascades in Large Scale Power Systems via the Influence Model Framework
Predicting Failure Cascades in Large Scale Power Systems via the Influence Model Framework
复制标题
基于影响模型框架的大规模电力系统故障级联预测
DOI：
10.1109/tpwrs.2021.3068409
复制
发表时间：
2021-09
影响因子：
6.6
通讯作者：
Xinyu Wu;Dan Wu;E. Modiano
中科院分区：
文献类型：
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作者：
Xinyu Wu;Dan Wu;E. Modiano
电网中的大规模停电往往是不受控制的故障连锁反应的结果。以高效且准确的方式预测故障连锁过程对于电力系统的应急分析非常重要。在本文中，我们提议将影响模型应用于大规模直流和交流电网中故障连锁的预测和筛选。然后，将经过训练的影响模型应用于一些拥有数千条母线和输电线路的大型电网。从整体故障规模的全局视角到有关链路故障时间的个体信息这四个不同方面对预测性能进行了评估。结果表明，在有限的训练样本下，所提出的框架能够以7%的误差率预测故障连锁规模，以10%的误差率预测链路的最终状态，并且对于直流和交流模型，故障时间都能在1个时间单位内。与基于潮流的应急分析相比，所提出方法的一个主要优势在于，它能够在精度有限降低的情况下，将故障连锁预测的计算时间减少几个数量级。所提出方法的另一个重要特征是，经过训练的影响参数能够揭示关键的初始故障。这些信息对于系统运营商识别最糟糕的故障场景非常有帮助。
Large blackouts in power grids are often the consequence of uncontrolled failure cascades. The ability to predict the failure cascade process in an efficient and accurate manner is important for power system contingency analysis. In this paper, we propose to apply the influence model for the prediction and screening of failure cascades in large scale DC and AC power networks. Then, the trained influence model is applied to some large power grids with thousands of buses and transmission lines. The prediction performance is evaluated in four different aspects, from the global perspective of the overall failure size to the individual information regarding the link failure time. The results show that under limited training samples, the proposed framework is capable of predicting the failure cascade size with a 7% error rate, the final state of links with a 10% error rate, and the failure time within 1 time unit for both the DC and AC model. One major advantage of the proposed method is that it can reduce the computational time of the failure cascade prediction by a few orders of magnitude with limited compromise in accuracy, as compared to the power flow based contingency analysis. Another important feature of the proposed method is that the trained influence parameters can reveal the critical initial contingencies. This information is very helpful for identifying the worst contingency scenarios for system operators.