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