LQ control of a three-phase four-wire shunt active power filter based on three-level NPC inverter
Bibliographic record
Abstract
This paper presents the design and implementation of a linear quadratic regulator (LQR) with Integral action (LQIR) for a three-phase four-wire shunt active power filter using a three-level neutral point clamped inverter (SAF-NPC). The integral action is added so as to cancel the steady state errors for reference tracking or disturbance rejection knowing that the standard LQR provides only proportional gains. The controller is designed to achieve overall DC bus voltage regulation, unbalance voltage between the upper and lower capacitors, harmonics, reactive power and loadpsilas neutral current compensations. The converter model is set in the d-q-o rotating reference frame. The latter is augmented with the integral of the q component of the SAF-NPC currents, DC bus voltage unbalance and overall DC bus voltage to achieve integral action. The controllerpsilas performance depends on the weighting matrix, which is chosen to ensure satisfactory responses. The converter is controlled as a multi-input multi-output (MIMO) system and a fixed PWM at 2.34 kHz is used to generate the gating signals of the power devices. The system is tested for harmonics, reactive power and load unbalance compensation for balanced/unbalanced loads. The simulation results obtained with SPS and Simulink of Matlab show good performance in terms of overall DC bus voltage regulation, DC bus voltage unbalance compensation (small overshoot and very fast time response) and a low total harmonic distortion (THD) of ac line currents.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".