# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1904.00433