Identifying Wind Farm Parameters via Dynamic Mode Decomposition With Control

Reliance on wind technology as a renewable energy source keeps increasing worldwide. This integration into the grid changes its characteristics and that necessitates accurate system modeling that requires parameter identification. The existing parameter identification methods are often obstructed by the presence of nonlinearities in the power system models, leading to a degradation in the prediction and computation. The fluid community proposed dynamic mode decomposition (DMD) as an effective system identification technique. The DMD prevails in several science applications, including power systems. The algorithm is enhanced to account for dynamical actuation data, which is referred to as dynamic mode decomposition with Control (DMDc). The DMDc provides superior accuracy in system identification. In this article, we propose the DMDc to identify the system parameters of a wind farm system, including an asynchronous machine, a rotor side controller, a dc link, and a grid-side controller. Further, we compare the DMDc with classical parameter identification methods: Prediction error method and the similarity matrix technique. Our results demonstrate the superiority of the proposed method in identifying the wind farm parameters with high accuracy and efficiency.