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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".