ANFIS Based Controller for Rectifier of PMSG Wind Energy Conversion System
Bibliographic record
Abstract
Wind energy is becoming a very useful energy source at present. With the increasing wind power penetration, improvements are required in order to comply with the grid interconnection requirements. The focus of this paper is to maximize the output power and address the output control of a utility-connected Permanent Magnet Synchronous Generator (PMSG) for wind power generation systems. PMSG has a back-to-back converter to control the output of the PMSG driven by the wind turbine. To supply commercially the power of WPGS to the grid without any problems related to power quality, the real and reactive powers of PMSG are strictly controlled at the required level. In this paper it is realized with the Adaptive Neuro Fuzzy Inference System (ANFIS) controller based on the field orientation control. The DC voltage of the DC link capacitor is also controlled at a certain level with the conventional Proportion-Integral controller of the real power. Studies demonstrate that the performance of the system with the ANFIS controller parameters permits an improvement of the converter capability and system performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".