The transition of the power grid requires new technologies and methodologies, which can only be developed and tested in simulations. Especially larger simulation setups with many levels of detail can become quite slow. Therefore, the number of possible simulation evaluations decreases. One solution to overcome this issue is to use surrogate models, i. e., data-driven approximations of (sub)systems. In a recent work, we built a surrogate model for a low voltage grid using artificial neural networks, which achieved satisfying results. However, there were still open questions regarding the assumptions and simplifications made. In this paper, we present the results of our ongoing research, which answer some of these questions. We compare different machine learning algorithms as surrogate models and exchange the grid topology and size. In a set of experiments, we show that algorithms based on linear regression and artificial neural networks yield the best results independent of the grid topology. Furthermore, adding volatile energy generation and a variable phase angle does not decrease the quality of the surrogate models.

中文翻译：

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评估不同的机器学习技术以替代低压电网

电网的过渡需要新技术和新方法，这些新技术和新方法只能在仿真中进行开发和测试。特别是具有许多细节级别的大型仿真设置可能会变得非常缓慢。因此，可能的仿真评估数量减少。克服此问题的一种解决方案是使用代理模型，即（子系统）的数据驱动近似。在最近的工作中，我们使用人工神经网络为低压电网建立了一个替代模型，取得了令人满意的结果。但是，关于假设和简化，仍然存在未解决的问题。在本文中，我们介绍了我们正在进行的研究的结果，这些结果回答了其中的一些问题。我们将不同的机器学习算法作为替代模型进行比较，并交换网格拓扑和大小。在一组实验中，我们证明了基于线性回归和人工神经网络的算法可获得最佳结果，而与网格拓扑无关。此外，增加挥发性能量的产生和可变的相角不会降低替代模型的质量。