A Novel Adaptive Neuro-Fuzzy Based Field Oriented Control of a Type-3 Based Wind Energy Conversion Systems with Improved Performance
Bibliographic record
Abstract
As the wind energy conversion system (WECS) has to cope with the wind speed uncertainties it is essential for a WECS to have improved dynamic and transient performances. Hence, in this paper an adaptive neuro fuzzy (ANF) based field-oriented control (FOC) scheme for Type-3 WECS is presented. This proposed ANF based FOC utilizes ANF networks to process the inputs. This proposed control scheme utilizes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d-q$</tex> axis stator current and compares with respective references to generate error signals. These error signals are further processed by the ANF networks to produce the d-q axis control voltage for rotor side converter of the WECS. The specific ANF structure and training algorithm is also established and presented in this paper. The effectiveness of the proposed ANF based FOC scheme is investigated and verified through simulation considering variable wind speed situation. The proposed scheme presents enhanced transient and dynamic performance for stator current, torque and rotor speed responses at variable wind speed situations. Furthermore, the proposed ANF based FOC scheme is implemented in real time in digital signal processing board DS 1104 and the efficacy of the proposed ANF based FOC scheme is demonstrated experimentally by applying the scheme on a Type-3 WECS prototype.
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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 | 0.000 |
| 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 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".