Optimization of DFIG Performance in Wind Energy Systems Using Fuzzy Logic Control and Harmonic Mitigation
Bibliographic record
Abstract
This paper proposes a comprehensive approach to optimize the performance of vector controlled Doubly Fed Induction Generator (DFIG) based wind energy systems by integrating fuzzy logic control and harmonic mitigation techniques.Renewable energy sources have gained interesting attention due to their environmental benefits, with wind energy emerging as a leading option for sustainable power generation.Among various wind turbine technologies, DFIG based systems are highly regarded for their variable speed operation, efficient energy capture and economic viability.In this study, a fuzzy logic controller (FLC) is developed to regulate the rotor part of the DFIG, ensuring precise and robust control of active and reactive power.Compared to conventional proportional-integral (PI) controllers, the FLC achieves 40% reduction in settling time and eliminates overshoot, enhancing the dynamic response and overall system stability.To address the harmonic distortions caused by power electronic voltage source converters (VSCs) at the point of common coupling (PCC), an LCL filter is employed to suppress unwanted harmonics and deliver cleaner sinusoidal waveforms.Furthermore, the integration of a multilevel VSC, controlled via Space Vector Pulse Width Modulation (SVPWM) with LCL filter, improves the output voltage waveform and minimizes Total Harmonic Distortion (THD).This dual control strategy harmonic mitigation combined with fuzzy logic-based rotor speed regulation ensures optimal power transfer, improved grid compliance and enhanced power quality.The proposed methodology is thoroughly analyzed, modeled and validated using MATLAB/Simulink.The results demonstrate significant improvements in DFIG system performance including reduced THD, better power quality and fast dynamic responses.This research offers a novel and practical solution for optimizing DFIGbased wind energy systems, contributing to the advancement of renewable energy technologies and grid integration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".