Improving the Characteristics of the Direct FOC Strategy in DFIG‐Based Wind Turbine Systems Using FOIDD and FOPD Controllers
Bibliographic record
Abstract
ABSTRACT The conventional direct field‐oriented control (DFOC) strategy using proportional–integral (PI) regulators for managing the energy of a doubly fed induction generator (DFIG) in wind turbine systems often proves inadequate due to the PI controller's sensitivity to parameter variations. Additionally, it tends to produce lower‐quality energy output. To address these shortcomings, this study proposes a novel control strategy that combines two fractional‐order controllers: a fractional‐order proportional‐derivative (FOPD) regulator and a fractional‐order integral dual‐derivative (FOIDD) regulator. These regulators are valued for their simplicity, low cost, and ease of implementation. The hybrid FOPD–FOIDD approach aims to enhance the performance and robustness of the traditional DFOC‐PI control applied to DFIG‐based wind turbine systems, enabling improved power regulation and dynamic response. To further optimize the designed control system, Particle Swarm Optimization is used to fine‐tune the controller parameters, ensuring efficient and stable power generation under varying and dynamic wind conditions. The new regulator replaces the classical PI in the DFOC scheme for the rotor‐side converter of the DFIG. The design and simulations were realized in MATLAB, and results were rigorously compared with those of the DFOC‐PI system under diverse operating conditions, including variations in active power reference, rapid wind speed changes, and parameter uncertainties. The comparative analysis demonstrates that the proposed FOPD–FOIDD controller significantly outperforms the DFOC‐PI. Simulation results show major improvements in dynamic performance, including reductions in current harmonic distortion by up to 87.55% and 14.14%, and substantial decreases in active power, torque, and reactive power ripples—by 93.18%, 92.42%, and 74.99%, respectively. Overall, the new control strategy exhibits superior robustness and stability, maintaining high‐quality power generation despite unpredictable variations in generator parameters.
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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) | 0.000 | 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 | 0.000 | 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".