Hybrid ANFIS-PI-Based Optimization for Improved Power Conversion in DFIG Wind Turbine
Bibliographic record
Abstract
Wind energy is essential for promoting sustainability and renewable power solutions. However, ensuring stability and consistent performance in DFIG-based wind turbine systems (WTSs) remains challenging due to rapid wind speed variations, grid disturbances, and parameter uncertainties. These fluctuations result in power instability, increased overshoot, and prolonged settling times, negatively impacting grid compliance and system efficiency. Conventional proportional-integral (PI) controllers are simple and effective in steady-state conditions, but they lack adaptability in dynamic situations. Similarly, artificial intelligence (AI)-based controllers, such as fuzzy logic controllers (FLCs) and artificial neural networks (ANNs), improve adaptability but suffer from high computational demands and training complexity. To address these limitations, this paper presents a hybrid adaptive neuro-fuzzy inference system (ANFIS)-PI controller for DFIG-based WTS. The proposed controller integrates fuzzy logic adaptability with neural network-based learning, allowing real-time optimization of control parameters. Implemented within the rotor-side converter (RSC) and grid-side converter (GSC), ANFIS enhances reactive power management, grid compliance, and overall system stability. The system was tested under a step wind speed signal varying from 10 m/s to 12 m/s to evaluate its robustness. The simulation results confirmed that the ANFIS-PI controller significantly improved performance compared with the conventional PI controller. Specifically, it reduced rotor speed overshoot by 3%, torque overshoot by 12.5%, active power overshoot by 2%, and DC link voltage overshoot by 20%. Additionally, the ANFIS-PI controller shortened settling time by 50% for rotor speed, by 25% for torque, by 33% for active power, and by 16.7% for DC link voltage, ensuring faster stabilization, enhanced dynamic response, and greater efficiency. These improvements establish the ANFIS-PI controller as an advanced, computationally efficient, and scalable solution for enhancing the reliability of DFIG-based WTS, facilitating seamless integration of wind energy into modern power grids.
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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) | 0.001 | 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 | 0.001 | 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".