A learning-augmented approach for AC optimal power flow
A learning-augmented approach for AC optimal power flow
复制标题
交流最佳潮流的学习增强方法
DOI：
10.1016/j.ijepes.2021.106908
复制
发表时间：
2021
影响因子：
5.2
通讯作者：
J. Zhang
中科院分区：
文献类型：
--
作者：
Jubeyer Rahman;C. Feng;J. Zhang
由于交流最优潮流的高度非线性，近几十年来人们一直在努力寻找有效的方法。机器学习（ML）已被证明可以显着降低许多现实问题的计算成本。因此，本文提出了一种学习增强的方法来求解交流最优潮流，它集成了电力网络方程和ML产生近最优解。更具体地，开发ML模型以首先预测总线电压幅度和角度。然后，基于物理的网络方程来计算在不同的总线的功率注入。三种ML算法，即，随机森林、多目标决策树和极限学习机等。为了评估建议的学习增强AC OPF求解器的效率，MATPOWER内部点求解器作为基线。对500节点和4918节点测试网络的案例研究表明，所提出的学习增强方法根据网络大小将计算时间减少了15-100倍，同时最优性损失最小。
Due to the high nonlinearity of AC optimal power flow (OPF), numerous efforts have been made in recent decades to find efficient methods. Machine learning (ML) has proven to significantly reduce the computational costs in many real-world problems. Thus, this paper develops a learning-augmented method for solving AC OPF, which integrates both power network equations and ML to yield near-optimal solutions. More specifically, ML models are developed to first predict bus voltage magnitudes and angles. Then, physics-based network equations are employed to calculate the power injection at different buses. Three ML algorithms, i.e., random forest, multi-target decision tree, and extreme learning machine, are explored and compared. To evaluate the efficiency of the proposed learning-augmented AC OPF solver, the MATPOWER Interior Point Solver is adopted as a baseline. Case studies on both 500-bus and 4918-bus test networks show that the proposed learning-augmented method has reduced the computational time by 15–100 times depending on the network size with a minimal loss in optimality.
混合学习辅助非活动约束过滤算法可缩短 AC OPF 求解时间
影响因子：
4.4
作者：
Hasan, Fouad;Kargarian, Amin;Mohammadi, Javad
通讯作者：
Mohammadi, Javad