Dual-Loop Geometric Control of Stator Flux for Improved LVRT Response in DFIG-Based Wind Turbine Systems
Bibliographic record
Abstract
Doubly Fed Induction Generator (DFIG) based wind turbinesare particularly sensitive to grid disturbances, which has implications for overall power system stability. Grid codes require continual operation of DFIGs despite certain system disturbances. During a three-phase voltage dip, the decay of a natural flux fixed to the stator of the machine induces large voltages in the rotor windings, leading to saturation of rotor converters and potential damage to the system. This work presents a novel dual-loop state-plane based architecture to solve the stator flux transient, allowing the system to regain standard operation and power reference tracking in a fast and effective manner. An outer loop geometric controller is proposed as a framework for achieving rapid and controllable transients in the decoupled state space, and various reference geometries are considered. The proposed control structure implements a unique continuous solution for both steady state and transient operating conditions and does not require any additional hardware, switching circuits, or fault detection mechanisms. Performance-based tuning is used to determine a controller that optimizes the performance index across a variety of grid fault levels, and the resultant controller is shown to achieve rapid transient response for all fault levels, with power reference tracking achieved in under one line cycle ($\text{20}\,\text{ms}$) for even the most extreme grid faults. The proposed controller is supported by detailed mathematical analysis and validated by simulation results.
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".