Development of a Novel Wind Turbine Simulator for Wind Energy Conversion Systems Using an Inverter-Controlled Induction Motor
Bibliographic record
Abstract
A wind turbine simulator for wind energy conversion systems has been developed with a view to design, evaluate, and test of actual wind turbine drive trains including generators, transmissions, power-electronic converters and controllers. The simulator consists of a 10-hp induction motor (IM) which drives a generator and is driven by a 10-kW variable speed drive inverter and real-time control software. In this simulator, a microcontroller, a PC interfaced to LAB Windows I/O board, and an IGBT inverter-controlled induction motor are used instead of a real wind turbine to supply shaft torque. A control program based on C language is developed that obtains wind profiles and, by using turbine characteristics and rotation speed of IM, calculates the theoretical shaft torque of a real wind turbine. Comparing with this torque value, the shaft torque of the IM is regulated accordingly by controlling stator current demand and frequency demand of the inverter. In this way, the inverter driven induction motor acts like a real wind turbine to the energy conversion system. The drive is controlled using the measured shaft torque directly, instead of estimating it as conventional drives do. The experimental results of the proposed simulator show that this scheme is viable and accurate. This paper reports the operating principles, theoretical analyses, and test results of this wind turbine simulator.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".