Cosimulation of Shifted-Frequency/Dynamic Phasor and Electromagnetic Transient Models of Hybrid LCC-MMC DC Grids on Integrated CPU–GPUs
Bibliographic record
Abstract
To effectively capture interactions of large-scale ac-dc systems integrating line commutated converter (LCC) and modular multilevel converter (MMC) based multiterminal dc grids, a numerically accurate and efficient simulation method is desirable. To achieve this objective, a cosimulation method is proposed in this article, where the target system is decoupled into the shifted-frequency phasor (SFP) subsystem, the dynamic phasor (DP) subsystem, and electromagnetic transient (EMT) subsystem, respectively. The MMCs are included in the SFP subsystem and implemented on massively paralleled graphics processing units (GPUs). Thus, the simulation efficiency is greatly improved by adopting a much larger time step, the model order reduction technique, and GPU acceleration. The LCCs are represented by DPs and are included in the DP subsystem. The majority of ac grids are covered in the EMT subsystem. Further, the interactions between SFP and EMT subsystems are reflected by the proposed multidomain transmission line model, which can produce instantaneous and phasor values simultaneously. The interface model between DP and EMT subsystems is modeled as a special controlled voltage and current circuit. Finally, the overall cosimulation method is realized by the respective SFP/DP and EMT models, among which their interactions are reflected by the proposed interface models and the time sequences of simulations. The performance of the proposed method has been fully validated on a practical large-scale ac-dc system.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".