Voltage flicker assessment in distribution feeders with large wind farms
Bibliographic record
Abstract
In recent years, Doubly Fed Induction Generator (DFIG) wind turbines connected to rural distribution feeders represents an emerging trend that has experienced growth.Higher penetration levels of embedded wind generation has interesting benefits (i.e.peak-shaving, congestion alleviation, reduction of losses, etc.) but raises important issues concerning the quality of power delivered to utility consumers.This thesis investigates the technical limitations involved with integrating large DFIG based wind farms into existing distribution feeders with regard to voltage flicker.This dissertation includes an overview of firstly, the applicable Electromagnetic Compatibility (EMC) standards related to the measurement and assessment of flicker emissions produced by distribution-connected wind farms.Secondly, aerodynamic, turbine and feeder characteristics which influence voltage flicker.Thirdly, the level of modeling required to conduct a pre-connection flicker study.Based on these three aspects, flicker emissions produced by a DFIG are quantified and a rule of thumb and a set of guidelines are presented for the acceptance of a 10 MW to 14 MW distributed wind farm, compliant to the allocated flicker emission quota.If the rule of thumb does indeed reveal a problem, both passive and active flicker mitigation techniques are proposed such that EMC of the power system is preserved.First and foremost, I would like to thank my supervisor, Prof. Géza Joós, for his continuous guidance and support throughout my engineering master's degree.Through the many discussions we had, his insight, knowledge and perspective have helped me develop the engineering skills required to tackle any challenge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".