Yang Seong-Gyu, Kim Beom Jun, Son Seung-Woo, Kim Heetae
Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea.
Department of Physics, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Chaos. 2021 Dec;31(12):123127. doi: 10.1063/5.0058001.
Complex network analyses have provided clues to improve power-grid stability with the help of numerical models. The high computational cost of numerical simulations, however, has inhibited the approach, especially when it deals with the dynamic properties of power grids such as frequency synchronization. In this study, we investigate machine learning techniques to estimate the stability of power-grid synchronization. We test three different machine learning algorithms-random forest, support vector machine, and artificial neural network-training them with two different types of synthetic power grids consisting of homogeneous and heterogeneous input-power distribution, respectively. We find that the three machine learning models better predict the synchronization stability of power-grid nodes when they are trained with the heterogeneous input-power distribution rather than the homogeneous one. With the real-world power grids of Great Britain, Spain, France, and Germany, we also demonstrate that the machine learning algorithms trained on synthetic power grids are transferable to the stability prediction of the real-world power grids, which implies the prospective applicability of machine learning techniques on power-grid studies.
复杂网络分析借助数值模型为提高电网稳定性提供了线索。然而，数值模拟的高计算成本阻碍了这种方法的应用，尤其是在处理电网的动态特性（如频率同步）时。在本研究中，我们研究了机器学习技术来估计电网同步的稳定性。我们测试了三种不同的机器学习算法——随机森林、支持向量机和人工神经网络——分别使用由均匀和异构输入功率分布组成的两种不同类型的合成电网对它们进行训练。我们发现，当使用异构输入功率分布而非均匀输入功率分布对这三种机器学习模型进行训练时，它们能更好地预测电网节点的同步稳定性。通过英国、西班牙、法国和德国的实际电网，我们还证明了在合成电网上训练的机器学习算法可转移到实际电网的稳定性预测中，这意味着机器学习技术在电网研究中具有潜在的适用性。
