Modeling and Fault Analysis of Doubly Fed Induction Generators for Gansu Wind Farm Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it