This paper presents for the first time, to our knowledge, a framework for verifying neural network behavior in 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 162-bus systems, treating both N-1 security and small-signal stability.

中文翻译：

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神经网络行为的验证：电力系统应用的正式保证

据我们所知，本文首次提出了在电力系统应用中验证神经网络行为的框架。到目前为止，神经网络已经在电力系统中被应用为黑匣子。这为它们在实践中的采用提供了主要障碍。基于混合整数线性规划开发的严格框架，我们的方法可以确定神经网络分类为安全或不安全的输入范围，并且能够系统地识别对抗性示例。这样的方法有可能在神经网络上建立电力系统运营商所缺乏的信任，并解锁电力系统中的一系列新应用。本文介绍了评估和提高电力系统中神经网络鲁棒性的框架，方法，并解决了与可伸缩性和准确性有关的问题。