InfiniBand-Based Real-Time Simulation of HVDC, STATCOM and SVC Devices with Custom-Of-The-Shelf PCs and FPGAs
Bibliographic record
Abstract
This paper presents a real-time simulator for large power network based on custom-of-the-shelf technologies, all embedded in the RT-LAB real-time simulation platform. This platform uses Pentium, Xeon, opteron-based PCs (multi-CPUs and/or dual-core configurations) or even Xilinx FPGA cards for computational engines and InfiniBand communication fabric for fast inter-PCs communications. The real-time PCs runs under well-known operating systems QNX or RedHawk Linux while the main user control interface is either Simulink or LabView. The paper demonstrates the real-time simulation of complete single-pole 12-pulse HVDC system on dual-CPU, dual-core 2.2 GHz Opteron PC under 15 microseconds time step. It also demonstrates the real-time simulation of complex power system devices like SVC, STATCOM and more general power systems like the Kundur network. The paper also discusses the latest advances in hardware-in-the-loop simulation like the possibility to program from within Simulink some controllers or devices, like a PMSM drive, directly in an FPGA card. This feature is enabled in RT-LAB by the use of Xilinx System Generator, a Simulink blockset. This FPGA targeting diminishes further the leap between prototype and production-type controller systems because the FPGA can implement rapidcontrol functions along with fast protection systems, like IGBT-current protection, of real controller
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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 | 0.000 | 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) | 0.006 | 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".