Modeling and Control Strategies for DFIG in Wind Turbines: A Comparative Analysis of SPWM, THIPWM, and SVPWM Techniques
Bibliographic record
Abstract
The paper presents a comprehensive study on modeling a doubly fed induction generator (DFIG) and explores various control strategies for pulse-width modulation (PWM) in back-to-back (B2B) converter techniques.A DFIG is characterized by a wound rotor and three slip-ring induction machines, with the stator winding directly connected to the power grid and the wound rotor interfaced with the grid through a 3-phase AC/DC/AC converter.Typically, the converter connected to the grid is referred to as the grid side converter (GSC), while the converter attached to the rotor's slip-ring circuit is termed the rotor side converter (RSC).This research delineates various PWM-based B2B converter methodologies applied to the DFIG within wind energy turbines, aiming to regulate the RSC for optimal power capture.The study employs MATLAB/SIMULINK for constructing a multi-phase voltage source converter two-level (VSC-2L) model, leveraging different PWM techniques including Sine-PWM, Sinusoidal-PWM with third harmonic injection (THIPWM), and space vector PWM (SVPWM).These techniques are assessed based on total harmonic distortion (THD) using fast Fourier transform (FFT) analysis.The findings indicate that SVPWM offers several advantages, such as ease of digital implementation, lower THD, reduced switching frequency losses, and more efficient utilization of the DC link voltage, thereby enhancing control strategy effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".