CPU/FPGA-Based Real-Time Co-Simulation Framework for Large-Scale Solar Power Plants
Bibliographic record
Abstract
This article proposes a co-simulation technique to enable coupling between the CPU and FPGA, aimed at overcoming the limitations of the fourth-generation (Gen 4) electric hardware solver (eHS) when using the built-in solar cell model from the Simscape/Power Systems (SPS) library in MATLAB/Simulink. In this paper, a large-scale solar power plant (LSSPP) is developed, consisting of two arrays along with associated control systems such as the current controller, power controller, and maximum power point tracking (MPPT) controller, for real-time simulation using co-simulation between CPU and FPGA. Specifically, all controllers and solar array models are implemented in the CPU environment, while the high-frequency power electronic converters and the external grid are modeled in the FPGA (or eHS) environment. For real-time simulation, the OP4510 real-time simulator from OPAL-RT Technologies and RT-LAB software are utilized, achieving a simulation time step as small as 295 ns. Three scenarios are examined: step changes in reference setpoints of controllers, switching for different frequencies (5 kHz to 100 kHz), and short-circuit faults. Finally, for a comprehensive assessment, these scenarios are validated using the SPS library in MATLAB/Simulink.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".