Tensor Decomposition based Adaptive Model Reduction for Power System Simulation

TL;DR

This paper introduces an adaptive tensor decomposition-based model reduction method to accelerate power system simulations while maintaining accuracy across varying load conditions.

Contribution

It presents a novel tensor decomposition approach for adaptive model reduction in power system simulations, improving speed and accuracy over traditional methods.

Findings

Significant speed-up in simulation times.

Maintains high accuracy during different load disturbances.

Effective reduction of model complexity using tensor techniques.

Abstract

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.