Optimal and Systematic Design of Current Controller for Grid-Connected Inverters
Bibliographic record
Abstract
A design approach for grid-connected inverter controllers for distributed and renewable energy system applications is proposed, where a high number of controller gains can be designed optimally in a fairly systematic way. The linear quadratic regulator problem is first modified to accommodate sinusoidal signal tracking through a meaningful state-space transformation. The cost function is modified to explicitly include the tracking error, so that its weights are designed in a transparent and systematic way. The proposed technique is applied to the well-known control structure comprising the fundamental and harmonic resonant controllers. Second, the control structure is rearranged to reject the distortions from both the grid voltage and the reference signal, and the proposed design technique is applied to this new structure. It is shown that the desired features of active damping for $LCL$ filters and robust performance against system uncertainties, harmonics, and disturbances are achieved. Third, the controller and the systematic design procedure are extended for inverters with $LLCL$ filters. Proposed control structures are designed and simulated and then implemented on a digital signal processor. Results confirm features of the method and its ability to address control system challenges in high power density and efficient inverter applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".