Model Order Reduction of Large-Scale Wind Farms: A Data-Driven Approach

TL;DR

This paper introduces a low-complexity, data-driven model order reduction method for large-scale wind farms that improves accuracy through two-sided moment matching, facilitating efficient integration into power grid simulations.

Contribution

The paper presents a novel data-driven MOR algorithm that achieves two-sided moment matching without high model knowledge, enhancing accuracy over existing methods.

Findings

The reduced models accurately replicate full models in frequency response.

The method doubles the accuracy of moment matching compared to standard approaches.

Validation on a 200-turbine wind farm shows effective integration into power system simulations.

Abstract

This paper proposes a data-driven algorithm for model order reduction (MOR) of large-scale wind farms and studies the effects that the obtained reduced-order model (ROM) has when this is integrated into the power grid. With respect to standard MOR methods, the proposed algorithm has the advantages of having low computational complexity and not requiring any knowledge of the high order model. Using time-domain measurements, the obtained ROM achieves the moment matching conditions at selected interpolation points (frequencies). With respect to the state of the art, the method achieves the so-called two-sided moment matching, doubling the accuracy by doubling the interpolated points. The proposed algorithm is validated on a combined model of a 200-turbine wind farm (which is reduced) interconnected to the IEEE 14-bus system (which represents the unreduced study area) by comparing the full-order model and the reduced-order model in terms of their Bode plots, eigenvalues and the point of common coupling voltages in extensive fault scenarios of the integrated power system.