Computationally Efficient Reduced Order Modeling of DFIG-Based Wind Turbines: A Novel Frequency-Weighted and Limited Model Reduction Approach With Error Bounds

Abstract

In wind turbine engineering, stability and control rely on precision. A new approach for discrete-time systems is presented in this study, which makes use of constrained Gramians and frequency weights. Wind turbines with a double-fed induction generator and dynamic rotational speeds can have their model order reduced using the suggested method, which makes use of sophisticated state-space representations. A novel balanced realization method, along with frequency-weighted and limited Gramians, successfully lowers the dimensionality of large state models. Minimizing approximation errors and ensuring stability are both achieved by the resulting lower-order system. This paper makes a significant contribution by offering an a priori formula for error boundaries, which allows for more efficient and faster computations. A paradigm shift in improving the accuracy of modeling techniques is marked by this groundbreaking method, which applies frequency-weighted and limited Gramians to real-time systems like wind turbines.

Keywords

• Frequency weighted Gramians
• frequency limited Gramians
• balance algorithm
• model reduction
• error-bound
• induction generator