Generalized Closed-Loop Control Schemes with Embedded Virtual Impedances for Voltage Source Converters with LC or LCL Filters
Bibliographic record
Abstract
In this paper, a generalized closed-loop control (GCC) scheme is proposed for voltage source converters (VSCs) with LC or LCL output filters. The proposed GCC scheme has a single-loop control of inverter output (voltage or current) and two parallel virtual impedance terms using additional measurements. The virtual impedance can be the equivalent internal impedance or external impedance (or both), depending on their control term and feedback variable selection. The internal impedance term is mainly responsible for providing desired damping to the filter circuit, and the external virtual impedance term can effectively adjust the converter system closed-loop output impedance. As each term in the GCC scheme can be controlled independently, the proposed GCC scheme has great flexibility and can easily realize and explain the performances of the traditional single- and multiloop control schemes and their different variations. Moreover, the GCC scheme provides a distinct physical meaning of each control term, which makes the control parameter tuning more straightforward and robust. Additionally, as shown in this paper, the proposed GCC scheme can tackle some traditionally challenging control objectives by avoiding the harmonics filtering or derivative terms. Experimental results from laboratory VSC prototypes are obtained to validate the proposed GCC scheme.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".