A Novel Intelligent Power Control Technique for a Type-3 Wind Energy Conversion System With LVRT Capability and Improved Dynamic Performance
Bibliographic record
Abstract
This paper presents a novel neuro-fuzzy (NF) based intelligent power control (IPC) technique for a Type-3 wind energy conversion system (WECS), which can achieve low voltage ride through (LVRT) capability by managing grid side disturbance of WECS. The proposed IPC technique considers the errors between command and actual values of real and reactive powers of the stator as inputs and processes these inputs through two NF networks to generated-qaxis switching signals for the switches of rotor side converters (RSCs). Additionally, a hybrid training method is developed to train the NF system parameters. The performance of the proposed control technique is tested in simulation under different grid disturbance and wind speed variations. Furthermore, a comparative study of performances is conducted between the proposed and the classical proportional-integral control technique at different operating conditions. Additionally, the stability of WECS and the effectiveness of the NF networks are investigated in this study. Furthermore, a laboratory prototype of Type-3 WECS is also built to test the performance of the proposed IPC technique in real-time using the DSP controller board DS-1104. Both the simulation and experimental results verify the LVRT capability of the IPC technique as well as its superior dynamic response with variations in wind speed.
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 | 0.000 | 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".