Frequency Limited & Weighted Model Reduction Algorithm With Error Bound: Application to Discrete-Time Doubly Fed Induction Generator Based Wind Turbines for Power System

• Main Authors: Sajid Bashir, Sammana Batool, Muhammad Imran, Mian Ilyas Ahmad, Fahad Mumtaz Malik, Muhammad Salman, Abdul Wakeel, Usman Ali
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Model reduction; frequency limited Gramians; frequency response error; error bound; wind turbine
• Online Access: https://ieeexplore.ieee.org/document/9316688/

The state-space representations grant a convenient, compact, and elegant way to examine the physical systems, e.g., induction and synchronous generator-based wind turbines, with facts readily available for stability, controllability, and observability analysis. In this article, the model order reduction of a stable doubly fed induction generator based variable-speed wind turbines model is performed with the aid of the proposed stability preserving balanced realization algorithm based on discrete frequency weights and limited frequency-interval. The frequency weighting and limited frequency-intervals-based model order reduction techniques presented by Enns's and Wang & Zilouchian produce an unstable reduced-order model at certain frequency weights and frequency intervals, respectively. To overcome this main drawback, many researchers provided a solution to preserve the stability of the reduced-order model. However, these existing approaches also produce an unstable reduced-order model in some conditions and produce a large variation to the original system; consequently, they provide a large approximation error. The proposed approach not only ensures the stability of the reduced-order model but also provides low approximation error as compared with other existing approaches and also provides an easily calculable a priori error bound formula. The proposed work produces steady and precise outcomes in contrast to conventional reduction methods, which shows the efficacy of the proposed algorithm.