Imperial College London > Talks@ee.imperial > Control and Power Seminars > Machine learning for power systems: Physics-Informed Neural Networks and Verification

Machine learning for power systems: Physics-Informed Neural Networks and Verification

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Fei Teng.

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. First, we introduce a framework for verifying neural network behavior in power systems. 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. 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. Second, we present a framework for physics-informed neural networks in power system applications. Exploiting the underlying physical laws governing power systems, and inspired by recent developments in the field of machine learning, we propose a neural network training procedure that can make use of the wide range of mathematical models describing power system behavior, both in steady-state and in dynamics. Physics-informed neural networks require substantially less training data and can result in simpler neural network structures, while achieving high accuracy. Methods such as the ones we will discuss in this talk unlock the potential of neural networks to perform power system tasks at extremely fast computing times while maintaining verified accuracy.

Bio: Spyros Chatzivasileiadis is an Associate Professor at the Technical University of Denmark (DTU). Before that he was a postdoctoral researcher at the Massachusetts Institute of Technology (MIT), USA and at Lawrence Berkeley National Laboratory, USA . Spyros holds a PhD from ETH Zurich, Switzerland (2013) and a Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece (2007). In March 2016, he joined the Center for Electric Power and Energy at DTU . He is currently working on machine learning applications for power systems, and power system optimization and control of AC and HVDC grids

This talk is part of the Control and Power Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

Changes to Talks@imperial | Privacy and Publicity