论文标题
评估不同的机器学习技术作为低压网格的替代技术
Evaluating Different Machine Learning Techniques as Surrogate for Low Voltage Grids
论文作者
论文摘要
电网的过渡需要新技术和方法，只能在模拟中开发和测试。尤其是具有许多细节级别的较大的模拟设置可能会变得非常慢。因此，可能的模拟评估数量减少。克服此问题的一种解决方案是使用替代模型，即（子）系统的数据驱动的近似值。在最近的一项工作中，使用人工神经网络建立了低压网格的替代模型，从而实现了令人满意的结果。但是，关于做出的假设和简化仍然存在开放的问题。在本文中，我们介绍了正在进行的研究的结果，这些研究回答了其中一些问题。我们将不同的机器学习算法与替代模型进行比较，并交换网格拓扑和大小。在一组实验中，我们表明，基于线性回归和人工神经网络的算法与网格拓扑无关。此外，添加挥发性能量产生和可变相角不会降低替代模型的质量。
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, a surrogate model for a low voltage grid was built 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 question. 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.
