Adaptive optimal control for parallel grid-connected inverters with LCL filters
Bibliographic record
Abstract
With the increasing penetration of renewable energy sources into modern power system especially in a microgrid form, power inverters are commonly employed as the interface to the utility grid. LCL-filters are commonly used in interfacing inverters, in order to reduce the harmonics around the switching frequency and its multiples. However, unlike the single grid-connected inverter system where the resonance frequency is mainly fixed by the inverter output LCL filter parameters, the parallel-inverter-based grid-interactive power system presents a more challenging picture where inverter interactions will excite complex resonances at various frequencies. In this paper, an adaptive optimal feedback control is proposed to address system uncertain multi-resonance problem in the parallel-connected inverter system with LCL filters. The proposed adaptive control will force the inverter by modifying state-feedback optimal control law adaptively to follow the desired system response, thus significantly improve the grid current quality under various grid conditions. Experimental results have verified the performance of the proposed adaptive control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".