A new reactive power management strategy to enhance the behavior of the Wind Turbine Generator driven a DFIG under grid faults
Bibliographic record
Abstract
This paper deals with the issue of the reactive power compensation in microgrid (MG) based on Wind Turbine Generator driving a Doubly-Fed Induction Generator (DFIG). Wind generators such as the majority of Distributed Generation (DG) systems are sensitive to the disturbances of the frequency and/or voltage. Subsequently, these systems must to be disconnected in case of voltage dip, typically where the voltage is less than 80% of rated value. DG which contributes to reduce energy loss during transmission can increase the capability of the voltage control in the power system by generating the reactive power support. In the case of the off-grid applications, sources of production may participate to the voltage control by providing or absorbing reactive power. In this work, a new strategy for determining the reference of reactive power in order to contribute to the correction of the grid voltage deviation during low or high voltage ride through is proposed. Then, the performances of both active and reactive power control of the DFIG under grid faults are analyzed through Matlab/Simulink software.
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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".