2024/12/13 by Zilong Gong, Junyu Mao, Gong, Zilong +5 · 1 citation
Engineering · #Energy Load and Power Forecasting #FOS: Electrical engineering #Real-time simulation and control systems #Systems and Control (eess.SY) #Wind Turbine Control Systems #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2412.10088
openalex publication_date 2024/12/13 · openalex created_date 2025/11/01 · openalex updated_date 2026/07/28
This paper proposes a data-driven algorithm for model order reduction (MOR)\nof large-scale wind farms and studies the effects that the obtained\nreduced-order model (ROM) has when this is integrated into the power grid. With\nrespect to standard MOR methods, the proposed algorithm has the advantages of\nhaving low computational complexity and not requiring any knowledge of the high\norder model. Using time-domain measurements, the obtained ROM achieves the\nmoment matching conditions at selected interpolation points (frequencies). With\nrespect to the state of the art, the method achieves the so-called two-sided\nmoment matching, doubling the accuracy by doubling the interpolated points. The\nproposed algorithm is validated on a combined model of a 200-turbine wind farm\n(which is reduced) interconnected to the IEEE 14-bus system (which represents\nthe unreduced study area) by comparing the full-order model and the\nreduced-order model in terms of their Bode plots, eigenvalues and the point of\ncommon coupling voltages in extensive fault scenarios of the integrated power\nsystem.\n