Impedance-Based Stability Analysis and Design of a Fractional-Order Active Damper for Grid-Connected Current-Source Inverters
Bibliographic record
Abstract
Due to its voltage boosting capability and current controllability, the current-source inverter (CSI) is a strong candidate for interfacing high-power photovoltaic (PV) systems with the utility grid. However, low-order harmonics from semiconductor switching or grid voltage give rise to resonance and render the converter unstable. Active damping techniques can modify the control algorithm to mitigate the resonance. However, digital control delays and grid-impedance variations complicate the active damping design and result in poor robustness. Notwithstanding recent research advancements, the technology of the CSI-based PV systems is still in its infancy, needing more attention to the control aspects. This paper proposes a fractional-order active damping control with a more tuning parameter for grid-connected CSI-based PV systems. A sound design strategy further has been presented for the fractional-order damper taking into account the digital control delay. The proposed active damper robustly mitigates the passive filter resonance and guarantees the power quality despite the grid-impedance variations. Simulation and experimental results demonstrate that the fractional-order active damper offers a superior response in comparison with a standard active damper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".