Modular Solid State Power Controller (SSPC) for High Voltage Aerospace Applications
Bibliographic record
Abstract
This paper introduces a novel modular Solid State Power Controller (SSPC) designed for high-voltage aerospace applications, addressing the industry's transition from the traditional 270 V standard to higher voltage systems. Each SSPC module is rated at$270 \mathrm{V} / 180 \mathrm{A}$, and leverages SiC MOSFET technology for enhanced switching speed, efficiency, and reliability. This study details the SSPC's architecture, key electrical components, and thermal management. The modular design enables flexible series and parallel configurations to meet a range of voltage and current demands. Experimental results demonstrate the SSPC's performance with up to four units connected in series and parallel, achieving a total blocking voltage of 1080 V and current carrying capability of 720 A, respectively. A custom-designed test bench generates highcurrent pulses to simulate fault conditions, while thermal management experiments validate continuous operation at rated current. Findings reveal precise synchronization during module switching and enhanced thermal performance through natural convection cooling of individual modules. This modular SSPC design presents a scalable, efficient, and reliable protection device for power distribution in next-generation aerospace systems, effectively addressing the evolving power demands of future aircraft applications.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".