Frequency Limited & Weighted Model Reduction Algorithm With Error Bound: Application to Discrete-Time Doubly Fed Induction Generator Based Wind Turbines for Power System
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 reduc...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9316688/ |
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| Summary: | 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 <italic>a priori error bound</italic> formula. The proposed work produces steady and precise outcomes in contrast to conventional reduction methods, which shows the efficacy of the proposed algorithm. |
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| ISSN: | 2169-3536 |