Neuro-Fuzzy Adaptive Direct Torque and Flux Control of a Grid-Connected DFIG-WECS With Improved Dynamic Performance
Bibliographic record
Abstract
This article presents an adaptive neuro-fuzzy interface system (ANFIS) based direct torque and flux (TF) control technique for manipulation of grid connected doubly fed induction generators (DFIG) in wind energy conversion systems (WECS). The proposed direct TF control technique generates PWM switching signals for the rotor side converter by comparing the actual torque and stator flux with their respective references so as to improve dynamic performance for the WECS. A hybrid training algorithm is proposed to adapt the ANFIS parameters to handle the WECS nonlinearities and wind speed uncertainties. The stability of the developed ANFIS is analyzed by modeling the WECS to a standard second order system. Initially, the effectiveness of the proposed ANFIS technique is examined by simulation under different operating conditions of the DFIG-WECS using MATLAB/Simulink. Then, a laboratory prototype of DFIG-WECS has been developed to investigate the real-time performance of the proposed direct TF control technique. Test results show that the proposed ANFIS direct TF control technique can provide more efficient performance compared to the related traditional techniques such as fuzzy and PI based schemes.
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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 | 0.000 |
| 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) | 0.001 | 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 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".