Robust three degrees of freedom based on <i>H</i> ∞ controller of voltage/current loops for DG unit in micro grids
Bibliographic record
Abstract
This study proposes a cascaded voltage–current robust control strategy for a distributed generator (DG) in microgrids. The main objective of the proposed controller is to improve the performance and power quality of a DG by injecting simultaneously a good sinusoidal voltage/current to the different loads connected to the DG. The proposed cascaded voltage/current controller consists of three degree‐of‐freedom controller, which is designed by using H ∞ control theory based on mixed sensitivity specifications. The choices of appropriate weighting functions W t , W u , W y that satisfy the preset goals to get a robust controller and a transform to a standard H ∞ control design problem based on pre‐compensator, feedforward and feedback connections are presented. The 3DOF‐controller is based on MATLAB R /SimPowerSystems and using RT‐EVENTS‐toolbox. The controller is evaluated under different scenarios: transient responses with a resistive local load, and loads disturbances in steady‐state responses with resistive, resistive inductive, resistive capacitive, and non‐linear loads. Simulation and experimental results of resulting waveforms from a DG unit are presented, it confirms the effectiveness of the proposed method in effectively rejecting perturbation and robustness to loads disturbances; both good reference tracking and good transient response, and finally significantly lead to a very low total harmonic distortion.
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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 | 0.000 |
| 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 | 0.000 | 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".