Distribution Network Resilience Enhancement with Topological Neural Networks

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The number of outages caused by severe weather as a result of climate change rose from about 50 annually nationwide in the early 2000s to over 100 annually on average over the past five years. With the increasing trend of cyber-physical threats and extreme weather events due to climate change, resilience of the power network has emerged as a problem of utmost societal importance. By facilitating the cross-disciplinary exchange of ideas, this project will develop a novel geometric deep learning model to significantly improve the security, reliability, and efficiency of the modern power grid by leveraging clean energy resources – thereby, impacting the safety and economic well-being of our society across a broad front. The project will provide training opportunities to graduate students. In particular, the PIs will develop a novel geometric deep learning model supported with topological data analysis tools for robust decision-making and efficient knowledge transfer for disruptions with time-dominant characteristics, particularly focusing on predicting the distribution network evolution and operational decisions (by leveraging distributed energy resources and clean energy resources) for resilience under disruptive events such as natural disasters and adversarial attacks. Envisioned gains in generalizability, robustness and learning efficiency via knowledge-transfer (across events and operation timescales) will be demonstrated by studying response and recovery operation problems in power grids. The developed model will be applied to enhance the resilience of the power distribution network under disruptive events, ranging from preparatory to restorative tasks. The pre-emptive measures adopted include (i) outage prediction and defensive islanding, while (ii) network reconfiguration is used as a restorative action.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.