AI4OPT Seminar Series: Machine Learning for Power Systems: Is It Time To Trust It?

Abstract: In this talk, we introduce methods that remove the barrier for applying neural networks in real-life power systems and unlock a series of new applications. More specifically, we introduce a framework that addresses five key challenges (dataset generation, data pre-processing, neural network training, verification, and embedding in other tools) associated with building trustworthy ML models which learn from physics-based simulation data. We introduce methods for (i) verifying neural network behavior in power systems and (ii) obtain provable worst-case guarantees of their performance. Up to this moment, neural networks have been applied in power systems as a black box; this has presented a major barrier for their adoption in practice. Using a rigorous framework based on mixed integer linear programming, our methods can determine the range of inputs that neural networks classify as safe or unsafe; and obtain provable worst-case guarantees of the neural network performance. Such methods have the potential to build the missing trust of power system operators on neural networks and unlock a series of new applications in power systems and other safety-critical systems.

Bio: Spyros Chatzivasileiadis is an Associate Professor at the Technical University of Denmark (DTU) and the Head of Section Power Systems (Division of Power and Energy Systems) at DTU Wind and Energy Systems. Before that, Chatzivasileiadis was a postdoctoral researcher at the Massachusetts Institute of Technology (MIT) and at Lawrence Berkeley National Laboratory. Spyros holds a Ph.D. from ETH Zurich, Switzerland (2013) and a diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece (2007). He is currently working on machine learning applications for power systems, as well as power system optimization, dynamics, and control of AC and HVDC grids. Chatzivasileiadis is also the recipient of the 2020 ERC Starting Grant.

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