System Modeling and Stability Analysis of Single-Phase Transformerless UPQC Integrated Input Grid Voltage Regulation
Bibliographic record
Abstract
This article extends the conventional features of a transformerless unified power quality conditioner (TL-UPQC). An enhanced control methodology is presented to allow exchanging reactive power between the system and the grid to provide input grid voltage regulation. Therefore, both load side and input grid-side voltages are regulated with one converter. In this regard, a phase angle is created between the input current and the input voltage. Thereby, the system behavior as a capacitive or an inductive reactance is controlled. An additional ac voltage control loop has been designed. The inner current loop has been reformed to receive reference information from two outer voltage loops. The enhanced control strategy takes action based on local information collected by the TL-UPQC with no requirements of additional sensor circuits. Since the conventional functions of the TL-UPQC system have been extended, aspects related to system modeling and control design should be developed. The small-signal model that characterizes the dynamics of the power stage and the controller are presented. Design guidelines considering grid impedance to achieve a desired performance are developed. A 500 VA / 120 V, 60 Hz prototype has been built to verify the models and the overall system performance. Steady-state and transient experimental results are presented and discussed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".