An Improved Single-Machine Equivalent Method of Wind Power Plants by Calibrating Power Recovery Behaviors
Bibliographic record
Abstract
An improved single-machine equivalent method is proposed for wind power plants (WPPs) by calibrating the postfault power recovery behaviors. A real WPP is simulated with extensive wind scenarios to evaluate the traditional single-machine equivalent model, and it is found that its equivalent error is mainly resulted from the postfault recovery process. The simulation analysis further indicates that a wind turbine operating at different wind speeds restores to its prefault active power at a certain ramp rate after fault clearance, with different starting power and different recovery time. Taking a two-machine WPP as an example, the analytical expression for active power of the WPP is derived for the fault ride through process, and thus the equivalent error of traditional single-machine model is found to be resulted from two aspects. One is the mismatch of the starting power of the postfault recovery process, and another is the mismatch of the power recovery rates between the complete WPP and its equivalent model. The analytical expressions are extended to multimachine WPP to calibrate the starting power and the ramp rates of the postfault recovery process. Simulation results show that the proposed method has good performance for various voltage drops, wind scenarios, and grid characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".