Application of Adaptive Network-Based Fuzzy Inference System for Sensorless Control of PMSG-Based Wind Turbine With Nonlinear-Load-Compensation Capabilities
Bibliographic record
Abstract
The precise information of permanent-magnet synchronous generator (PMSG) rotor position and speed is essentially required to operate it on maximum power points. This paper presents an adaptive network-based fuzzy inference system (ANFIS) for speed and position estimation of PMSG, where an ANFIS-based model reference adaptive system is continuously tuned with actual PMSG to neutralize the effect of parameter variations such as stator resistance, inductance, and torque constant. This ANFIS-tuned estimator is able to estimate the rotor position and speed accurately over a wide speed range with a great immunity against parameter variation. The proposed system consists of two back-to-back connected inverters, where one controls the PMSG, while another is used for grid synchronization. Moreover, in the proposed study, the grid-side inverter is also utilized as harmonic, reactive power, and unbalanced load compensator for a three-phase, four-wire (3P4W) nonlinear load, if any, at point of common coupling (PCC). This enables the grid to always supply/absorb a balanced set of fundamental currents at unity power factor. The proposed system is developed and simulated using MATLAB/SimPowerSystem (SPS) toolbox. Besides this, a scaled laboratory hardware prototype is developed and extensive experimental study is carried out to validate the proposed control approach.
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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 | 0.000 | 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".