Real-Time Emulation of a Grid-Connected Wind Energy Conversion System Based Double Fed Induction Generator Configuration under Random Operating Modes
Bibliographic record
Abstract
This paper presents the design, modeling, simulation and the experimental implementation of a 1.5 kW relatively low-cost wind energy conversion system (WECS) based on the double fed induction generator (DFIG) configuration. In the preliminary experiments, we test the DFIG power control under fixe speed by applying the vector control principle, then we insert the physical emulator presented in the controlled DC motor in order to simulate the static-dynamic behaviors of a real wind turbine with the use of the Tip Speed Ratio TSR based MPPT algorithm to extract the maximum available power on the emulator. The proposed structure is simulated using MATLAB Simulink environment, the obtained results are validated experimentally on our laboratory setup. We also develop an application with MATLAB AppDesigner that calculates the operating point of our system at steady state and visualize the power transfer, current, voltage and electromagnetic torque values of the DFIG and the DC motor before starting the stimulation or the experimental manipulation. The MPPT, the DC motor control and the DFIG power control algorithms are implanted in C, embedded on a dSPACE DS1104 control board. The obtained results confirm the reliability of the proposed WECS to manage all the probable operating modes, also the effectiveness of the physical simulator in the role of wind turbine emulation.
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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 | 0.000 |
| 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) | 0.002 | 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".