Optimal Power Reserve of a Wind Turbine System Participating in Primary Frequency Control
Bibliographic record
Abstract
Participation of a wind turbine (WT) in primary frequency control (PFC) requires reserving some active power. The reserved power can be used to support the grid frequency. To maintain the required amount of reserve power, the WT is de-loaded to operate under its maximum power. The objective of this article is to design a control method for a WT system to maintain the reserved power of the WT, by controlling both pitch angle and rotor speed simultaneously in order to optimize the operation of the WT system. The pitch angle is obtained such that the stator current of the permanent magnet synchronous generator (PMSG) is reduced. Therefore, the resistive losses in the machine and the conduction losses of the converter are minimized. To avoid an excessive number of pitch motor operations, the wind forecast is implemented in order to predict consistent pitch angle valid for longer timeframe. Then, the selected pitch angle and the known curtailed power are used to find the optimal rotor speed by applying a nonlinear equation solver. To validate the proposed de-loading approach and control method, a detailed WT system is modeled in Matlab/Simulink (The Mathworks, Natick, MA, USA, 2017). Then, the proposed control scheme is validated using hardware-in-the-loop and real time simulation built in Opal-RT (10.4.14, Opal-RT Inc., Montreal, PQ, Canada).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".