Adaptive Nonlinear Model Reduction for Fast Power System Simulation

TL;DR

This paper introduces an adaptive nonlinear model reduction method for power system simulation that dynamically switches between detailed, linear, and nonlinear reduced models to improve speed and accuracy during different system states.

Contribution

It presents a novel adaptive approach that balances linear and nonlinear model reduction techniques for efficient and accurate power system simulations.

Findings

The adaptive method improves simulation speed during stable periods.

It maintains high accuracy during fault conditions.

Case studies demonstrate superior performance over traditional linear reduction.

Abstract

The paper proposes a new adaptive approach to power system model reduction for fast and accurate time-domain simulation. This new approach is a compromise between linear model reduction for faster simulation and nonlinear model reduction for better accuracy. During the simulation period, the approach adaptively switches among detailed and linearly or nonlinearly reduced models based on variations of the system state: it employs unreduced models for the fault-on period, uses weighted column norms of the admittance matrix to decide which functions to be linearized in power system differential-algebraic equations for large changes of the state, and adopts a linearly reduced model for small changes of the state. Two versions of the adaptive model reduction approach are introduced. The first version uses traditional power system partitioning where the model reduction is applied to a defined large external area in a power system and the other area defined as the study area keeps full detailed models. The second version applies the adaptive model reduction to the whole system. The paper also conducts comprehensive case studies comparing simulation results using the proposed adaptively reduced models with the linearly reduced model on the Northeast Power Coordinating Council 140-bus 48-machine system.