Construction and Grid-Connection Control Verification of SVG Simulation Model for Power Collection Systems
Bibliographic record
Abstract
With the increasing grid-connection applications of power collection systems, their power electronic devices are prone to introducing reactive power loss, which affects the power quality of the power grid. A Static Var Generator (SVG) is therefore required to ensure grid-connection performance. This paper designs the SVG main circuit for power collection systems based on a voltage-source bridge circuit. Under ideal assumptions, a mathematical model in the abc coordinate system is established, where the on-off characteristics of devices are described by switching functions. A decoupled model in the dq coordinate system is then derived through 3s/2s and 2s/2r coordinate transformations. A grid voltage-oriented double closed-loop control strategy is adopted: the outer loop stabilizes the DC-side voltage using a PI controller, the inner loop tracks reactive current, and an intermediate voltage is introduced to eliminate variable coupling. Meanwhile, SPWM and SVPWM modulation modules are constructed. A simulation model is built based on MATLAB/Simulink (grid line voltage 400V, load 200kW active power/100kvar reactive power, grid-connection inductor 1mH, DC voltage 800V). The results show that: without SVG, the grid power factor is 0.894; after SVG operation, the voltage and current phases align within 0.15s, the power factor approaches 1, and the DC voltage stabilizes at 800V within 0.07s. The current THD is 1.49% with SPWM modulation and decreases to 1.30% with SVPWM. The research indicates that the SVG simulation model can meet the reactive power compensation requirements of power collection system grid-connection, improving the power quality and stability of the grid-connection side.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".