A Wind Turbine Emulator that Represents the Dynamics of the Wind Turbine Rotor and Drive Train
Bibliographic record
Abstract
A wind turbine emulator (WTE) is an important equipment for developing wind energy conversion systems. It offers a controllable test environment that allows the evaluation and improvement of control schemes for electric generators what is hard to achieve with an actual wind turbine since the wind speed varies randomly. A WTE consists essentially of a torque controlled electric motor and a reference torque calculator. The latter usually reproduces only the average torque of the wind turbine. This paper presents an improved version that considers also the harmonic torques due to the gradient and tower shadow effects, inertia of the wind turbine and elasticity of the drive train. It is intended to be used for the analysis of remedial solutions for damping harmonic torques produced by the wind turbine and drive train resonances. Basic equations that represent the dynamic system are derived and used to implement the control scheme. A laboratory prototype was built with a permanent magnet DC motor driven by either a three-phase thyristor rectifier or a pulse width modulated (PWM) DC-DC converter and a digital signal processing (DSP) system. The experimental results demonstrate the effectiveness of the reference torque calculator, identifies inherent limitations imposed by the phase-controlled thyristor rectifier and eliminates the problems using a DC-DC converter
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".