Dorcas Ofori-Boateng

TL;DR: Findings show that local topological summaries can successfully reflect changes in the grid resilience, and compare the TDA summaries with the power system reliability metrics, to show this.

TL;DR: In this article, the authors introduce a new topological perspective to structural anomaly detection in dynamic multilayer networks by invoking clique persistent homology on graphs to systematically and efficiently track evolution of the network shape and, as a result, detect changes in the underlying network topology and geometry.

TL;DR: This work proposes a distribution-free framework for anomaly detection in dynamic networks and adopts a change point detection method for (weakly) dependent time series based on efficient scores, and enhances the finite sample properties of change point method by approximating the asymptotic distribution of the test statistic using the sieve bootstrap.

TL;DR: The concepts of topological data analysis, particularly, persistent homology, are used to derive a new metric for resilience of power grid networks, illustrated in application to a simulated version of the Texas power grid network.

TL;DR: In this article, the authors proposed a new topology-based system that relies on a neural network architecture for impact metric classification and prediction in power systems and evaluated the impact of three power system contingency types, in conjunction with transmission lines, transformers, and transmission lines combined with transformers.