Machine learning algorithms, especially Neural Networks (NNs), are a valuable tool used to approximate non-linear relationships, like the AC-Optimal Power Flow (AC-OPF), with considerable accuracy -- and achieving a speedup of several orders of magnitude when deployed for use. Often in power systems literature, the NNs are trained with a fixed dataset generated prior to the training process. In this paper, we show that adapting the NN training dataset during training can improve the NN performance and substantially reduce its worst-case violations. This paper proposes an algorithm that identifies and enriches the training dataset with critical datapoints that reduce the worst-case violations and deliver a neural network with improved worst-case performance guarantees. We demonstrate the performance of our algorithm in four test power systems, ranging from 39-buses to 162-buses.

翻译：机器学习算法，尤其是神经网络，是用于逼近非线性关系（如交流最优潮流）的有价值工具，其精度相当高，且在部署使用时能实现数个数量级的加速。在电力系统文献中，神经网络通常采用训练前生成的固定数据集进行训练。本文表明，在训练过程中调整神经网络的训练数据集能够提升其性能，并显著减少最坏情况下的违规。本文提出一种算法，该算法识别并向训练数据集中补充关键数据点，从而减少最坏情况违规，并交付具有更优最坏情况性能保障的神经网络。我们在四个测试电力系统（涵盖39节点至162节点）上展示了该算法的性能。