论文标题
DeepOpf-al：用于解决多个负载映射的AC-OPF问题的增强学习
DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings
论文作者
论文摘要
非凸AC-OPF问题的多个负载分解映射的存在对深神经网络（DNN）方案构成了根本挑战。由于训练数据集可能包含与不同负载量映射相对应的数据点的混合物，因此DNN无法学习合法的映射并生成劣质解决方案。我们建议DeepOpf-al作为解决此问题的增强学习方法。这个想法是训练DNN，以学习从增强输入（即（负载，初始点））的唯一映射到由具有负载和初始点作为进气口的迭代OPF求解器生成的解决方案。然后，我们将学习的增强映射应用于求解AC-OPF问题的速度要快得多。与最近的DNN方案相比，IEEE测试案例的模拟结果表明，DeepOPF-AL可以明显取得更好的最优性，相似的可行性和加速性能，具有相同的DNN尺寸，但培训的复杂性提升了。
The existence of multiple load-solution mappings of non-convex AC-OPF problems poses a fundamental challenge to deep neural network (DNN) schemes. As the training dataset may contain a mixture of data points corresponding to different load-solution mappings, the DNN can fail to learn a legitimate mapping and generate inferior solutions. We propose DeepOPF-AL as an augmented-learning approach to tackle this issue. The idea is to train a DNN to learn a unique mapping from an augmented input, i.e., (load, initial point), to the solution generated by an iterative OPF solver with the load and initial point as intake. We then apply the learned augmented mapping to solve AC-OPF problems much faster than conventional solvers. Simulation results over IEEE test cases show that DeepOPF-AL achieves noticeably better optimality and similar feasibility and speedup performance, as compared to a recent DNN scheme, with the same DNN size yet elevated training complexity.
