Modeling and Fault Analysis of Doubly Fed Induction Generators for Gansu Wind Farm Application
Bibliographic record
Abstract
Wind power is developing rapidly as a means of handling the world's energy shortage and associated environmental problems. The Gansu provincial wind energy resources have around 237-GW wind power potential in China. In this paper, a study on key technologies of Hexi 750-kV power transmission line protections has been carried out. The project includes some characteristics, such as large-scale wind power, long-distance EHV lines, and so on. We used 49.5-MW doubly fed induction generator (DFIG) wind turbines in this project and different situations when a fault occurs in the presence of DFIG are studied and investigated. By the aid of stator-flux-oriented vector strategy, the system is modeled in PSCAD/EMTDC software on the basis of the real information from the wind farm site. The fault analysis is studied while the fault location is changed and the crowbar protection is ON/OFF. The data and information have been obtained by field experience of the wind farm in Gansu province. Also, the matrix pencil algorithm has been applied as a novel method in this project. This analysis can ease the protection issues and push the schedule to the next steps. With these results, we are able to adapt our system with smart grids and provide some novel methods to stabilize and control the wind farm.
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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 | 0.001 | 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".