Jochen Cremer / Imperial College London

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    • Title: The Machine Learning Approach to Dynamic Security Assessment

    Date: December 2, 2019

    Bio: Jochen Cremer is a final year PhD student in the Control and Power Group (CAP) of the Department of Electrical and Electronic Engineering at Imperial College London. Before joining CAP group in 2017 he undertook research in mathematical optimization and control theory at Carnegie Mellon and MIT. He is an engineer at heart and holds a M.Sc. in Chemical Engineering, a B.Sc. in Electrical Engineering and a B.Sc. in Mechanical Engineering from RWTH Aachen University, Germany. His research interest lies in the intersection of machine learning and mathematical optimization applied to the operation of the power system.

    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.

    Learning Materials: Talk Flyer Talk Slides

    Interacting Materials:

    1. Papers
      1. Machine Learning Models for Online Dynamic Security Assessment of Electric Power Systems
      2. Application of Machine Learning on Power System Dynamic Security Assessment
      3. Power Systems Dynamic Security Assessment using machine learning
      4. Machine learning approach to power system dynamic security analysis
      5. Application of Machine Learning on Power System Dynamic Security Assessment
      6. Machine Learning Models for Online Dynamic Security Assessment of Electric Power Systems
    2. Videos
      1. Dynamic Application Security Testing (DAST)
      2. Dynamic Application Security Testing
      3. IEEE BDA Webinar Series: Big Data & Analytics for Power Systems
      4. Transient Stability Assessment Tools, DSA Tools Subsystem
      5. Security Attacks: Analysis of Machine Learning Models
      6. Secure DevOps with Automated DAST
    3. Data


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