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Evaluating different machine learning techniques as surrogate for low voltage grids

机译：评估不同的机器学习技术作为低电压网格的代理

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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.
机译：电网的转换需要新技术和方法，只能在模拟中开发和测试。尤其是具有许多级别细节的仿真设置可能会变得非常慢。因此，可能的模拟评估的数量降低。克服这个问题的一个解决方案是使用代理模型，我。 e，数据驱动的（子）系统的近似。在最近的工作中，我们使用人工神经网络为低电压电网构建了代理模型，从而实现了满足结果。但是，关于假设和简化仍有开放的问题。在本文中，我们介绍了我们正在进行的研究的结果，这些问题回答了这些问题。我们将不同的机器学习算法与代理模型进行比较并交换网格拓扑结构和尺寸。在一组实验中，我们表明基于线性回归和人工神经网络的算法产生了与网格拓扑无关的最佳结果。此外，增加挥发性能量产生和可变相位角不会降低代理模型的质量。

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