Predicting Failure Cascades in Large Scale Power Systems via the Influence Model Framework

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 work, 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 under metrics with different granularity. 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 inaccuracy, 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.

This is joint work with Dan Wu, advised by Eytan Modiano.