LQR with integral action applied to a wind energy conversion system based on doubly fed induction generator
Bibliographic record
Abstract
This paper presents the design of a Linear Quadratic Regulator (LQR) with Integral action (LQRI) applied to a wind energy conversion system (WECS) based on doubly fed induction generator (DFIG). The controller is used to achieve speed regulation for maximum power extraction from the wind turbine, DC bus voltage regulation, to make the machine draw its magnetizing current only from the stator and to keep the power factor at unity for the grid-side converter. The model of this latter is set in the d-q rotating reference frame synchronized with phase a of grid voltages while stator flux oriented vector control is used for the rotor-side converter. Knowing that the standard LQR provide essentially proportional gains, the DFIG dynamic is augmented with the integral of the q component of the grid-side converter currents, the d component of the machine-side converter currents, DC bus voltage and machine's speed in order to cancel steady-state errors. The DFIG is controlled as a whole i.e. a multi-input-multi-output (MIMO) system and a fixed PWM at 5 kHz is used to generate the gating signals of the two back to back converters. The system is tested for both sub-synchronous and super-synchronous speed and the simulation results obtained with Sim Power System (SPS) and Simulink of Matlab implementation of the controller proved the effectiveness of the control strategy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".