Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications

Andreas Venzke, Spyros Chatzivasileiadis

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    Abstract

    This paper presents for the first time, to our knowledge,aframeworkforverifyingneuralnetworkbehaviorin power system applications. 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. Developing 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 are able to systematically identify adversarial examples. 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. This paper presents the framework, methods to assess and improve neural network robustness in power systems, and addresses concerns related to scalability and accuracy. We demonstrate our methods on the IEEE 9-bus, 14-bus, and 162bus systems, treating both N-1 security and small-signal stability.
    Original languageEnglish
    JournalIEEE Transactions on Smart Grid
    Volume12
    Issue number1
    Pages (from-to)383 - 397
    ISSN1949-3053
    DOIs
    Publication statusPublished - 2021

    Keywords

    • Neural networks
    • Mixed-integer linear programming
    • Security assessment
    • Small-signal stability

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