一种具有硬约束的神经网络学习框架，用于快速从直流最优功率流恢复交流最优功率流
A hard-constrained NN learning framework for rapidly restoring AC-OPF from DC-OPF
AI总结：
本文提出了一种硬约束神经网络学习框架，用于快速从直流OPF恢复交流OPF，实现40倍时间加速和高精度约束满足。
摘要：
本文提出了一种硬约束无监督学习框架，用于在实时运行中快速解决非线性和非凸的交流最优功率流（AC-OPF）问题。不需要真实的AC-OPF解决方案，通过精心设计的学习环境和训练损失来确保可行性和最优性。受残差学习启发，神经网络（NN）通过重新调度学习从直流最优功率流（DC-OPF）解决方案到发电机有功功率设定点的修正映射。随后使用一个优化模型来恢复最优的AC-OPF解决方案，所得到的投影差异被用作训练损失。通过使用回放缓冲区，利用过去的数对数据完全提高学习效率。该优化模型被表示为可微优化层，其中梯度通过应用隐函数定理来推导KKT条件在最优解处的梯度。在IEEE-118和PEGASE-9241配电系统上测试，数值结果表明，所提出的NN能够获得严格可行且近最优的解决方案，计算时间比传统优化求解器减少。此外，借助在变化拓扑下更新的DC-OPF解决方案，训练好的NN与PF求解器相结合，可以快速找到相应的AC解决方案。所提出的方法实现了40倍的时间加速，同时保持平均约束违反在10^-4的数量级，优化差距低于1%。
英文摘要：
This paper proposes a hard-constrained unsupervised learning framework for rapidly solving the non-linear and non-convex AC optimal power flow (AC-OPF) problem in real-time operation. Without requiring ground-truth AC-OPF solutions, feasibility and optimality are ensured through a properly designed learning environment and training loss. Inspired by residual learning, the neural network (NN) learns the correction mapping from the DC-OPF solution to the active power setpoints of the generators through re-dispatch. A subsequent optimization model is utilized to restore the optimal AC-OPF solution, and the resulting projection difference is employed as the training loss. A replay buffer is utilized to enhance learning efficiency by fully leveraging past data pairs. The optimization model is cast as a differentiable optimization layer, where the gradient is derived by applying the implicit function theorem to the KKT conditions at the optimal solution. Tested on IEEE-118 and PEGASE-9241 bus systems, numerical results demonstrate that the proposed NN can obtain strictly feasible and near-optimal solutions with reduced computational time compared to conventional optimization solvers. In addition, aided by the updated DC-OPF solution under varying topologies, the trained NN, together with the PF solver, can rapidly find the corresponding AC solution. The proposed method achieves a $40\times$ time speedup, while maintaining an average constraint violation on the order of $10^{-4}$ and an optimization gap below $1\%$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
