Analysis and Mitigation of Inner Voltage Control’s Negative Damping Effect on Active Power Oscillations in Multi-VSG Systems
Bibliographic record
Abstract
The virtual synchronous generator (VSG), a grid-forming technology, is emerging as an effective approach to improving frequency and voltage regulation in power grids with high penetrations of inverter-based resources. In a system with multiple parallel VSGs, two types of active power oscillations can occur: one is between the multi-VSG system and the grid (VSG-gridoscillation); the other is among different VSGs within the system (inter-VSGoscillation). Existing studies have shown that these oscillations primarily result from VSGs' emulation of the swing equation. However, the impact of VSG's inner voltage control (IVC) on these oscillations remains inadequately examined. To provide physical insights into this issue, this article proposes damping torque analysis models for theVSG-gridandinter-VSGoscillations. The models reveal that the IVC introduces negative damping torques to both types of oscillations, potentially leading to oscillatory instability. To address this issue, a graphical IVC parameter design method is proposed. This method defines a satisfactory region that ensures consistently positive net damping torques for both theVSG-gridandinter-VSGoscillations, thereby effectively mitigating oscillatory instability in multi-VSG systems. Finally, real-time simulations and experiments verify the theoretical analyses and the parameter design method.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".