Enhanced Control of Doubly Fed Induction Generator Based Wind Turbine System Using Fractional-Order Fuzzy PD+I Regulator
Bibliographic record
Abstract
This paper introduces an innovative control strategy for wind turbine systems (WTS) based on doubly fed induction generators (DFIGs).The strategy employs a fractional-order fuzzy PD+I (FO Fuzzy PD+I) regulator, which is optimized using the social spider optimizer (SSO) algorithm.This approach marks a significant advancement in DFIG control compared to existing methods that rely on traditional PI regulators.The proposed FO Fuzzy PD+I regulator leverages the combined strengths of fuzzy logic and fractional-order control, resulting in superior performance and robustness in DFIG current control.It effectively addresses uncertainties in DFIG parameters and wind speed variations, while enabling independent active and reactive power regulation for enhanced grid integration and power quality management.The efficacy of the proposed approach is validated through simulations across diverse operational scenarios, encompassing step changes in active power reference and rapid fluctuations in wind speed.The optimized FO Fuzzy PD+I regulator consistently outperforms the traditional PI regulator in terms of integral time absolute error (ITAE), peak overshoot, maximum undershoot, settling time, and total harmonic distortion (THD) of DFIG current.This research represents a significant contribution to the field of DFIG control, offering a more effective and robust solution for wind turbine operation, ultimately leading to improved power quality and grid integration capabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".