Abstract
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.
Abstract (translated)
机器学习算法，特别是神经网络(NNs)，是一种宝贵的工具，用于近似非线性关系，如交流最优能量流(AC-OPF)，具有相当准确的精度，并在部署时实现数 orders of magnitude 的提速。通常在电力系统文献中，NNs 通常是在训练过程开始前生成固定的数据集进行训练。在本文中，我们表明，在训练期间适应NN训练数据集可以改进NN性能，并显著减少其最坏情况下的违反。本文提出了一种算法，可以识别并丰富训练数据集中的关键数据点，以减少最坏情况下的违反，并生成NNs 具有改进最坏情况下性能保证。我们展示了我们算法在四个测试电力系统中的表现，这些电力系统的规模从39辆到162辆不等。
URL
https://arxiv.org/abs/2303.13228
