Design of an inverter-side current reference and controller for a single-phase LCL-based grid-connected inverter
Bibliographic record
Abstract
This paper presents the design of a current reference used in an inverter-side current controller for a grid-connected single-phase transformerless inverter with an Inductive-Capacitive-Inductive of LCL filter. The idea is to indirectly control the inverter-side current, by directly acting on the grid-side current. Furthermore, it can be shown that the simple proportional term guarantees damping injection. However, the design of the current reference for such a controller is a more involved process, which turns out to be even more challenging in the case where the grid voltage signal is subject to harmonic distortion. In this case, the distortion propagates into the converter through the LCL filter. Therefore, an inverter-side current reference must be proposed to be as distorted as necessary to assure indirect tracking of the grid-side current towards its corresponding reference, which is built as a sinusoidal with a given phase displacement to allow reactive current injection. The inverter-side controller must then include a harmonic compensation mechanism, to assure perfect tracking of the inverter-side current towards its distorted reference, and thus, achieve an adequate performance under harmonic distortion. Experiments are performed in a prototype to evaluate the performance of the proposed controller.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".