Development of a Novel Real-Time Weighted Surrogate Model for Wind Turbine-Based on DFIG: Enhancing Computational Efficiency in Power Systems With Error Bound

In the realm of modern wind turbine engineering, where precision is paramount for stability, control, and observability, this article introduces a groundbreaking method leveraging time-weighted Gramians. The focal point of this work is the reduction of model order in wind turbines featuring a double-fed induction generator with time-varying rotational speeds. Employing sophisticated state-space representations, we establish a comprehensive and systematic framework for analyzing wind turbine performance, ensuring adherence to stringent grid standards. A distinctive aspect of our proposed approach lies in the utilization of time-weighted Gramians and an innovative balanced realization technique to reduce the dimensionality of large state-space models effectively. Stability and reduced approximation errors are guaranteed by creating a lower-order system. A significant improvement is the availability of an a priori formula for error-bound, which allows for more efficient and faster computations. The use of time-weighted Gramians allowed for the application of this groundbreaking technique to time-sensitive systems in the real world, such as wind turbines. The optimization of models utilizing vast simulation data is what makes our methodology better than current methods. Wind turbines that allow for real-time adjustment of rotating speeds are part of this dataset. A lot of consideration is given to stability and error calculation in this study, which makes it innovative. The use of time-weighted Gramians in practical, real-time systems has greatly improved the accuracy and efficiency of modeling approaches.