Title: The Machine Learning Approach to Dynamic Security Assessment
Date: December 2, 2019
Bio: Simon Tindemans is an assistant professor at Delft University of Technology in the Netherlands,
and a visiting researcher at the Alan Turing Institute in London, UK. He has previously worked at
Imperial College London, where he was a Marie Curie Intra-European Fellow and Research Fellow. He
has a background in theoretical biophysics, with an MSc in physics (cum laude) from the University of
Amsterdam and a PhD from AMOLF/Wageningen University (2009). His research interests include
machine learning for risk assessment, the efficient computation and apportioning of risks, and methods
for the aggregate dispatch of flexible resources. He is an active member of the IEEE Risk Reliability
and Probability Applications sub-committee and its working groups and task forces.
Abstract: The integration of large amounts of renewable energy makes flows of electrical power more
variable and less predictable. This presents a large computational challenge for system operators, who
must anticipate and mitigate insecure operating conditions using detailed time-domain simulations of
the physical system.
In this tutorial we present a machine learning approach to addressing this challenge that is increasingly
being used in the research community. After formulating the problem of dynamic security assessment
(DSA), we show how time-consuming simulations can partially be replaced by machine-learned proxies
or emulators. This approach separates the workflow into two parts: offline training and online
application. For the offline part, we cover the process of learning classifiers, and methods to generate
training data. For the online part, we describe novel approaches to control the risk due to imperfect
proxies, and a means of embedding the proxies in the calculation of optimal control actions.