Nonlinear Model Reduction in Power Systems by Balancing of Empirical Controllability and Observability Covariances

TL;DR

This paper introduces a nonlinear model reduction technique for power systems that balances empirical controllability and observability covariances directly on the nonlinear system, improving efficiency and accuracy over linearized methods.

Contribution

The paper presents a novel nonlinear model reduction method using empirical covariances without linearizing the system, enhancing accuracy and efficiency in power system simulations.

Findings

Significant reduction in computation time.

State trajectories closely match full system simulations.

Higher accuracy than linearized balanced truncation.

Abstract

In this paper, nonlinear model reduction for power systems is performed by the balancing of empirical controllability and observability covariances that are calculated around the operating region. Unlike existing model reduction methods, the external system does not need to be linearized but is directly dealt with as a nonlinear system. A transformation is found to balance the controllability and observability covariances in order to determine which states have the greatest contribution to the input-output behavior. The original system model is then reduced by Galerkin projection based on this transformation. The proposed method is tested and validated on a system comprised of a 16-machine 68-bus system and an IEEE 50-machine 145-bus system. The results show that by using the proposed model reduction the calculation efficiency can be greatly improved; at the same time, the obtained state trajectories are close to those for directly simulating the whole system or partitioning the system while not performing reduction. Compared with the balanced truncation method based on a linearized model, the proposed nonlinear model reduction method can guarantee higher accuracy and similar calculation efficiency. It is shown that the proposed method is not sensitive to the choice of the matrices for calculating the empirical covariances.