A Fast Firing Angle Optimization Approach for Current-Controlled Switched Reluctance Generators in Wind Power Applications
Bibliographic record
Abstract
The adequate choice of excitation parameters is detrimental to the high performance operation of switched reluctance generators. Such task, however, is a complex problem which lacks solutions with simple analytical formulations. In this context, this paper presents a performance optimization procedure for switched reluctance generators operating in the current controlled region, below base speed. The proposal allows optimal firing angles to be determined based on the particle swarm optimization algorithm. A cost function is designed as a means to ensure performance with a compromise between reduced torque ripple and increased energy efficiency. A comparison with a traditional exhaustive search algorithm is provided, highlighting the reduced computational complexity of the proposal. Moreover, an original statistical analysis is presented as a means to demonstrate the low dispersion of the PSO-based procedure. Experimental results are provided in order to demonstrate the performance of the wind energy conversion system operating with optimal parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".