Parameter estimation of doubly fed induction generator driven by wind turbine
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
In order to reduce the environmental consequences of electric power generation, there has been a growing interest in the use of renewable resources for generating electricity. One way of generating electricity from renewable sources is to use wind turbines that convert the kinetic energy contained in the flowing air into electrical energy. As wind power is integrated in large scale European and North American power systems, investigating the dynamic behavior of these turbines is of great importance. Unfortunately, the parameters of the wind turbine needed to conduct dynamic analysis are frequently unknown or inaccurate. This paper analyzes the behavior of two Kalman filter based estimation techniques, the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), for parameter estimation of the doubly fed induction generator (DFIG) driven by wind turbine. The performance of these two methods is evaluated from different aspects: estimation accuracy, computation time, and robustness to variation of the initial parameter estimates and filter gains. Our experiments show that the performance of the UKF is superior to that of the EKF.
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 | 0.000 | 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 it