A Review on Switched Reluctance Generators in Wind Power Applications: Fundamentals, Control and Future Trends
Bibliographic record
Abstract
With the ever growing environmental concerns, renewable energy sources emerge as a promise of clean and abundant energy, enabling long-term sustainable development. In this context, wind power gained significant interest due to its relative low cost and availability. Switched reluctance generators (SRGs) are suitable candidates for wind energy conversion systems, as they present a simple structure, robustness, a wide range of speed and are capable of operating in harsh environments. The machine, however, poses challenges such as high torque ripple, acoustic noise production and highly nonlinear behavior. Nonetheless, with the use of adequate control strategies, high dynamic performance SRG-based wind energy conversion systems can be achieved. As a result, this article presents a state of the art review of SRGs in wind power applications. First, the fundamentals of the SRG are presented. Next, two categories of firing angle control are reviewed: optimization and closed-loop control. Then, voltage and power control strategies are discussed, being divided in model-independent and model-based approaches. After that, a review on grid-tied SRG-based wind energy conversion systems is carried out. The most common filter topologies as well as the employed control strategies are detailed. Lastly, an outline of the discussed topics is presented and future trends as well as suggestions for future investigation are listed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".