A Neuro-Fuzzy Based Power Control of a Type-3 based Wind Energy Conversion Systems with LVRT Capability
Bibliographic record
Abstract
The Type-3 Wind Energy Conversion Systems (WECS) incorporating doubly fed induction generators (DFIG) are highly affected by grid side disturbance as the stator side of the machine is connected to the grid directly. Furthermore, it is difficult for WECS to maintain satisfactory performance at normal grid condition, due to abrupt change in wind speed. The conventional PI based control strategies are not competent to manage grid disturbances as well as wind speed fluctuations. Consequently, a neuro-fuzzy based power control (NFPC) scheme for a grid connected Type-3 WECS is presented in this paper. The controller is designed to achieve low voltage ride-through (LVRT) capabilities as well as to maintain robust performance at the time of wind speed fluctuation. A hybrid training method is also derived to train the NF network parameters. The proposed NFPC scheme is simulated using MATLAB-Simulink and the performance of the Type-3 WECS is investigated considering several types of grid disturbances as well as wind speed fluctuation. Satisfactory performances are obtained in terms of different quantities of the WECS at the time of grid disturbances as well as the wind speed fluctuations. The trajectories of the real and reactive power are also analyzed to check the stability of the entire WECS due to incorporating NFPC scheme. The system stability is also found satisfactory both for grid disturbance as well as wind speed change. A laboratory prototype of Type-3 WECS is built to test the real-time performance of the proposed NFPC technique using the DSP board DS 1104.
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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".