基于非线性参与因子的电力系统模型降阶方法应对近共振条件
Nonlinear Participation Factor-based Power System Model Reduction Addressing Near-Resonance Conditions
- Department of EECS, University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出基于非线性参与因子的自适应降阶方法，通过张量收缩加速计算，在近共振条件下实现电力系统仿真提速并保持精度优于线性方法。
AI中文摘要：
本文提出了一种基于非线性参与因子（NPF）的自适应模型降阶方法，该方法能够确定电力系统中需要线性化的最有效不重要发电机组选择，以加速整个系统的时域仿真。该方法实现了全阶模型与混合降阶模型之间的动态转换。它利用模态能量在故障条件下对系统模态进行排序，并基于正规形理论计算高能模态的NPF。为加速NPF的计算并使其能够在线实现，引入了一种张量收缩技术。所提方法在48机、140节点的NPCC系统上采用分区和非分区两种策略进行了测试。结果表明，在系统非线性行为不可忽略的情况下，尤其是存在近共振条件时，该方法能够显著提升仿真速度，同时比基于线性参与因子的方法保持更好的精度。
英文摘要：
This paper proposes an adaptive model reduction approach based on nonlinear participation factors (NPFs) which determines the most effective selection of unimportant generators in a power system to be linearized to accelerate time-domain simulation for the entire system. The method enables a dynamic transition between the full-order model, and a hybrid reduced model. It uses modal energies to rank system modes under contingencies, and computes NPFs for highly energized modes based on Normal Form theory. To accelerate the computation of NPFs and make it achievable online, a tensor contraction technique is introduced. The proposed approach is tested on a 48-machine, 140-bus NPCC system using both partitioned and unpartitioned strategies. It demonstrates significant simulation speedup while preserving better accuracy than a linear participation factor-based method if the nonlinear behaviors of the system cannot be ignored, especially when a near-resonance condition is presented.
