FPGA-based simulation of grid-tied converters using frequency-dependent network equivalent
Bibliographic record
Abstract
This paper introduces a real-time simulation framework for grid-tied converters, implemented on field-programmable gate arrays (FPGAs). The framework incorporates a Frequency-Dependent Network Equivalent (FDNE) to reduce the original part of the circuit that is not directly under study into a frequency-dependent admittance model, enabling precise modeling of the power network’s frequency-dependent dynamics while streamlining the onboard simulation and modeling process. The proposed framework is implemented on the Alveo U280 FPGA, achieving sub-microsecond latencies, low resource utilization, and high computational fidelity across various data types, including single-, double-precision, and customized floating-point formats. The numerical test and validation were conducted using a high-voltage power network that includes detailed models of transmission lines, loads, and a Static Synchronous Compensator (STATCOM), etc. Simulation results show strong alignment with reference models developed in the EMTP, achieving faster-than-real-time performance. These findings demonstrate the effectiveness of the proposed solution in delivering high-speed, resource-efficient, and scalable real-time simulations, providing a promising approach for testing and validating advanced control strategies in modern power systems.
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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) | 0.005 | 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".