Low-Inertia Microgrid Synchronization Using Data-Driven Digital Twins
Bibliographic record
Abstract
We introduce data-driven and scalable digital twins (DTs) and decentralized observer-based control (DOBC) to enhance inverter synchronization in low-inertia microgrids. The proposed DT, serving as cyber-physical replicas, enables real-time monitoring and data-driven control. We employed the Kuramoto model as a reduced-order dynamic representation of the low inertia inverter-based microgrid. Additionally, we used finite state machines (FSM) to digitally integrate the states and operating modes of virtual oscillator controls (VOC) inverters and microgrid dynamics. To address potential inconsistencies in data acquisition, we implemented generative adversarial imputation nets (GAIN) for the imputation of missing states in real-time. For inverter synchronization and minimizing the control efforts in the presence of the grid topology changes and interruptions, we applied Gramian localized approximation (GLA) and DOBC. These techniques helped us identify an optimal subset of inverters for control. The efficacy of this approach was validated through several scenarios for normal operation and fault isolation cases. The proposed DT model with DOBC significantly reduces synchronization time to under 9 seconds for average to large topology connections, compared to conventional PLL methods requiring around 3 minutes.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".