Research and development of VSC power disturbance generator
Bibliographic record
Abstract
In order to provide various disturbance voltage waveforms for the test of power quality event detection and compensation device, it is necessary to develop a device that can simulate power grid faults.VSC power disturbances generator (VSC-IG, Interruption Generator based on Voltage Source Converter) is the importance of theoretical study and compensation device of power quality test tools, the flexibility to produce all kinds of disturbance voltage waveform, And reduce the harmonic pollution to the power grid.In this paper, the power injected by AC power supply into VSC-IG is controlled to stabilize the DC voltage, and a PI controller parameter tuning method considering the change of resistance parameters is proposed to optimize the dynamic performance of the controller.The SPWM rule sampling method is adopted as the underlying control strategy to realize the generation of disturbance voltage waveform.PSCAD/EMTDC platform is used to build the VSC-IG simulation model and carry out the simulation research.The VSC-IG device is designed and implemented by using the physical prototype hardware platform of 30kVA back-to-back converter in dynamic modeling laboratory.The experiment verifies the main circuit structure and control strategy of VSC-IG in this paper, and also verifies the function of digital controller, which lays a foundation for further research on power quality.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".