Neuro-Fuzzy Vector Control for Doubly-Fed Wind Driven Induction Generator
Why this work is in the frame
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Bibliographic record
Abstract
Wound-rotor induction generator has numerous advantages in wind power generation over other types of generators. One scheme is realized when a converter cascade is used between the slip-ring terminals and the utility grid to control the rotor power. This configuration is called the doubly-fed induction generator (DFIG). A vector control scheme is developed to control the rotor side voltage source converter. This scheme allows the independent control of the generated active and reactive power as well as the rotor speed to track the maximum wind power point. In this work, a neuro-fuzzy gain tuner is proposed to control the DFIG. Vector control is used to allow independent control of the generator speed, active and reactive power. Six neuro-fuzzy gain tuners are used, two for each controlled variable. The input for each neuro-fuzzy system is the error value of speed, active or reactive power. The choice of only one input to the system simplifies the design and reduces the computational burden while giving excellent overall system performance.
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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 | 0.000 |
| 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 it