Tensor Decomposition based Adaptive Model Reduction for Power System Simulation
For power system operators needing faster simulations, this method offers adaptive complexity control, but the improvement over existing methods is incremental.
The paper proposes an adaptive model reduction method using tensor decomposition to speed up power system simulation, achieving a compromise between speed and accuracy. On the 140-bus 48-machine system, it outperforms traditional linear model reduction.
The letter proposes an adaptive model reduction approach based on tensor decomposition to speed up time-domain power system simulation. Taylor series expansion of a power system dynamic model is calculated around multiple equilibria corresponding to different load levels. The terms of Taylor expansion are converted to the tensor format and reduced into smaller-size matrices with the help of tensor decomposition. The approach adaptively changes the complexity of a power system model based on the size of a disturbance to maintain the compromise between high simulation speed and high accuracy of the reduced model. The proposed approach is compared with a traditional linear model reduction approach on the 140-bus 48-machine Northeast Power Coordinating Council system.