论文标题
通过模型订单降低和Neumann系列扩展，电分配网络中的概率功率流加速了
Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion
论文作者
论文摘要
本文开发了一种计算高效的算法，该算法通过利用电源配电网络中电压曲线的固有低级别性质来加快概率功率流（PPF）问题。因此，该算法被称为Accelerated-PPF（APPF），因为它可以加速“基于”基于“”采样的PPF求解器。随着APPF的运行，它同时生成了矫正溶液向量的低维子空间。该子空间用于构建和更新整个非线性系统的减少订单模型（ROM），从而为将来的电压配置文件提供了高效的仿真。在构建和更新子空间时，必须在完整的非线性系统上解决功率流问题。为了加速这些解决方案的计算，实施了修改功率流的诺伊曼（Neumann）扩展。当注射量很小时，适用于这种Neumann扩展，可以在标准的牛顿迭代期间提高Jacobian系统的速度。最终介绍了从完整的IEEE 8500节点测试馈线上运行的实验中的AppF测试结果。
This paper develops a computationally efficient algorithm which speeds up the probabilistic power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage profile in electrical power distribution networks. The algorithm is accordingly termed the Accelerated-PPF (APPF), since it can accelerate "any" sampling-based PPF solver. As the APPF runs, it concurrently generates a low-dimensional subspace of orthonormalized solution vectors. This subspace is used to construct and update a reduced order model (ROM) of the full nonlinear system, resulting in a highly efficient simulation for future voltage profiles. When constructing and updating the subspace, the power flow problem must still be solved on the full nonlinear system. In order to accelerate the computation of these solutions, a Neumann expansion of a modified power flow Jacobian is implemented. Applicable when load bus injections are small, this Neumann expansion allows for a considerable speed up of Jacobian system solves during the standard Newton iterations. APPF test results, from experiments run on the full IEEE 8500-node test feeder, are finally presented.
