2019/03/31 by Denis Vasilievich Osipov, Kai Sun, Osipov, Denis +1
Computer Science · Engineering · #Computational Physics and Python Applications #Dynamical Systems (math.DS) #FOS: Electrical engineering #FOS: Mathematics #Power System Optimization and Stability #Power Systems and Technologies #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1904.00433
openalex publication_date 2019/03/31 · openalex created_date 2022/07/29 · openalex updated_date 2026/08/01
The letter proposes an adaptive model reduction approach based on tensor\ndecomposition to speed up time-domain power system simulation. Taylor series\nexpansion of a power system dynamic model is calculated around multiple\nequilibria corresponding to different load levels. The terms of Taylor\nexpansion are converted to the tensor format and reduced into smaller-size\nmatrices with the help of tensor decomposition. The approach adaptively changes\nthe complexity of a power system model based on the size of a disturbance to\nmaintain the compromise between high simulation speed and high accuracy of the\nreduced model. The proposed approach is compared with a traditional linear\nmodel reduction approach on the 140-bus 48-machine Northeast Power Coordinating\nCouncil system.\n