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Efficient creation of datasets for data-driven power system applications
Electric Power Systems Research ( IF 3.9 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.epsr.2020.106614
Andreas Venzke , Daniel K. Molzahn , Spyros Chatzivasileiadis

Advances in data-driven methods have sparked renewed interest for applications in power systems. Creating datasets for successful application of these methods has proven to be very challenging, especially when considering power system security. This paper proposes a computationally efficient method to create datasets of secure and insecure operating points. We propose an infeasibility certificate based on separating hyperplanes that can a-priori characterize large parts of the input space as insecure, thus significantly reducing both computation time and problem size. Our method can handle an order of magnitude more control variables and creates balanced datasets of secure and insecure operating points, which is essential for data-driven applications. While we focus on N-1 security and uncertainty, our method can extend to dynamic security. For PGLib-OPF networks up to 500 buses and up to 125 control variables, we demonstrate drastic reductions in unclassified input space volumes and computation time, create balanced datasets, and evaluate an illustrative data-driven application.

中文翻译:

为数据驱动的电力系统应用高效创建数据集

数据驱动方法的进步重新激发了人们对电力系统应用的兴趣。事实证明,为成功应用这些方法创建数据集非常具有挑战性,尤其是在考虑电力系统安全性时。本文提出了一种计算高效的方法来创建安全和不安全操作点的数据集。我们提出了一种基于分离超平面的不可行性证书,可以先验地将输入空间的大部分表征为不安全,从而显着减少计算时间和问题规模。我们的方法可以处理更多数量级的控制变量,并创建安全和不安全操作点的平衡数据集,这对于数据驱动的应用程序至关重要。虽然我们专注于 N-1 安全性和不确定性,但我们的方法可以扩展到动态安全性。
更新日期:2021-01-01
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