Multiple feedback loop control strategy for single-phase voltage-source UPS inverter
Bibliographic record
Abstract
This paper investigates the performance of multiple feedback loop control strategy for single-phase voltage-source UPS inverter with an L-C filter. In order to select appropriate feedback variables and assess the stability of the closed loop operation of the overall system, the power circuit (inverter and filter plus load) incremental dynamics is investigated using the state-space averaging technique and root locus method. The results of the stability analysis show that a control scheme which employs the filter capacitor current in an inner feedback loop and the load voltage in an outer voltage control loop results in successful operation of the UPS system. Computer simulation results of a single-phase voltage-source half-bridge UPS inverter with a second order filter and R-L load is presented to demonstrate the performance of the proposed control scheme. Experimental verification of a laboratory model of the UPS system is also provided for both linear and nonlinear loads so as to verify the predicted performance of the system. It is shown that the control scheme offers improved performance measures over existing schemes. It is simple to implement and capable of producing nearly perfect sinusoidal load voltage waveform at moderate switching frequency and reasonable size of filter parameters. Furthermore, the scheme has fast dynamic response and high voltage utilization of the DC source.>
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".