Projects
Understanding the response of power grids under weather-related attacks - New*
Power grids are prone to a broad range of disruptive events, e.g., man-made threats, as in the case of terrorist attacks and cyber-threats, as well as from natural hazards such as earthquakes, hurricanes, and severe storms. Understanding the power grid’s response and dynamics under such disruptive events is critical towards developing timely and efficient risk mitigation strategies. In this project we focus on assessing power grid’s resilience under weather-related threats. Based on the structural makeup of power grids, we intend to quantify the structural state of the grid under varying weather hazardous scenarios. Consequently, the proposed measures can be integrated into early warning indicators for the power grid’s failure as well as employed for developing timely and efficient risk mitigation strategies.
Topological Anomaly Detection of Dynamic Networks
This research focuses on constructing novel nonparametric methods for anomaly detection on dynamic networks using the emerging mathematical machinery of topological data analysis. In particular, we describe shapes of complex networks via analysis of simplicial complexes and then track graph structural shifts from the resulting topological footprints over time. We validate our methods in applications to networks from social sciences, telecommunication and blockchain transactions.
Topological Machine Learning Methods for Power System Responses to Contingencies
To provide fast acting synthetic regulation and contingency reserve services to power grids, while having minimal disruptions on customer quality of service, we propose a new topology-based system that depends on neural network architecture for impact metrics classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, namely, in conjunction with transmission lines, transformers, and transmission lines combined with transformers.
Multi-resolution Data Matching through Lenses of Topological Data Analysis
Topological data analysis (TDA) combines concepts from algebraic topology, machine learning, statistics, and data science that allows us to study data in terms of their latent shape properties. In spite of the numerous application of TDA in a broad range of applications, from neuroscience to power systems to finance, the utility of TDA in the Earth Science applications is yet untapped. The current study aims to offer a new direction to analysis of multi-resolution Earth science data using the concept of data shape and the associated intrinsic topological data characteristics. Our results show that contrary to the existing approaches, TDA allows for systematic and reliable comparison of spatial patterns from various observational and model datasets without regridding the datasets into common grids.