Instantaneous Current-Sharing Control Strategy for Parallel Operation of UPS Modules Using Virtual Impedance
Bibliographic record
Abstract
By regulating the inverter output current every switching cycle, instantaneous current-sharing control strategies are usually employed in paralleled modular uninterruptible power supplies (UPSs). In this paper, virtual impedance, which is usually utilized in the droop method, is firstly introduced to the instantaneous current-sharing control strategy to achieve good load current-sharing performance. The instantaneous circulating current model of the paralleled system is developed and the circulating impedance is derived as well to clearly identify the intrinsic nature of unequal sharing of load current. By inserting the virtual impedance to the circulating impedance with the help of proper control strategy, good current-sharing capability can be obtained. The parallel inductor, e.g., coupled inductor, which is generally added at the output of each inverter to reduce the circulating current among modules, can be, therefore, eliminated, contributing to reduced weight, volume, and cost. In the meantime, the system output-voltage regulation performance, e.g., amplitude and waveform quality, is not affected by the introduction of the virtual impedance. The designation of the virtual impedance is provided and its implementation is simple but rather effective. Experimental results are also provided to verify the feasibility of the proposed method.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".