论文标题
数据驱动的最佳功率流：一种物理知识的机器学习方法
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach
论文作者
论文摘要
本文提出了一种基于堆叠的极限学习机（SELM）框架的数据驱动方法（OPF）的方法。 SELM具有快速的训练速度，与深度学习算法相比，不需要耗时的参数调整过程。但是，由于系统操作状态与OPF解决方案之间的复杂关系，SELM在OPF中的直接应用是无法处理的。为此，开发了一个数据驱动的OPF回归框架，将OPF模型功能分解为三个阶段。这不仅降低了学习的复杂性，还有助于纠正学习偏见。还开发了基于主动约束识别的样本前分类策略，以实现增强的特征景点。在IEEE和波兰基准系统上进行的数值结果表明，所提出的方法的表现优于其他替代方案。还表明，可以通过仅调整几个高参数来轻松扩展所提出的方法来解决不同的测试系统。
This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. SELM has a fast training speed and does not require the time-consuming parameter tuning process compared with the deep learning algorithms. However, the direct application of SELM for OPF is not tractable due to the complicated relationship between the system operating status and the OPF solutions. To this end, a data-driven OPF regression framework is developed that decomposes the OPF model features into three stages. This not only reduces the learning complexity but also helps correct the learning bias. A sample pre-classification strategy based on active constraint identification is also developed to achieve enhanced feature attractions. Numerical results carried out on IEEE and Polish benchmark systems demonstrate that the proposed method outperforms other alternatives. It is also shown that the proposed method can be easily extended to address different test systems by adjusting only a few hyperparameters.
