NSF Award
2319243
Neural Networks (NNs) have revolutionized the way we operate and manage modern power systems, providing remarkable solutions to modeling complex non-linear relationships and performing pattern recognition tasks using abundant data collected by state-of-the-art monitoring sensors. Despite the promising advantages, the efficiency and reliability of these models can be negatively impacted by noisy or biased power measurements and the unpredictability of renewable energy sources. The NN-based models are further complicated by their inherent non-linear, high-dimensional nature and vulnerability to adversarial attacks. Recognizing the risks associated with empirical methods that lack formal robustness guarantees, especially in a field where model failures can lead to disastrous real-world consequences, this project seeks to enhance the security and reliability of power systems by optimizing the cutting-edge NN verifier (alpha, beta-CROWN) tailored to the characteristics of modern power systems. The resulting improvements aim to provide power grid operators with safe, dependable tools to operate the power systems. Moreover, this project also intends to support education and research initiatives, encompassing the fields of machine learning and power system, for both bachelor's and master's degree students.<br/><br/>With a vision to bridge the existing gap between the power systems and the robust neural network verification techniques, this project is divided into three thrusts. In Thrust I, the project will extend the applications of NN verifiers to topology-aware power systems, examining different scenarios that include complete and incomplete verification on various model structures and adjusting branch and bound heuristics accordingly. Thrust II will enhance the effectiveness of current NN verifiers by incorporating power system static and dynamic constraints and further improve verification efficiency through certifiable training. Lastly, in Thrust III, the project will develop specially designed verifiers for power systems to serve as a novel tool for sensitivity analysis-based power system planning. This last component incorporates verification approaches for the first time, utilizing explainable Artificial Intelligence within power systems. Collectively, these research efforts will revolutionize people’s understanding and application of formal robustness verification techniques to power systems, ensuring the security and dependability of modern power networks.<br/><br/>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.
