Research on Cabin Integrated Equipment Based on GIS
Bibliographic record
Abstract
Through the research on the principle of GIS gas insulated switchgear, a GIS-based cabin integrated equipment method is proposed. The insulation characteristics and engineering calculations of SF6 gas are analyzed, which provides a basis for the selection of 220kV and 110kV equipment. Analyzed the applicability of 220kV equipment selection of HGIS equipment and GIS equipment in modular substations. At the same time, it compared the floor area and other indicators, and finally decided to adopt the GIS plan, further optimize the product structure and layout, and improve space utilization It can reduce the equipment footprint and make the layout of the power distribution device more compact. Through the comparison and analysis of the main wiring types of the 110kV single bus three section and double bus wiring, it is found that there are three aspects of reliability, flexibility and economy. Single-bus three-section is better than double-bus wiring, so 110kV is determined to adopt single-bus three-section wiring. Under the premise of meeting the requirements for power supply safety, reliability, and flexibility, 110kV electrical main wiring is optimized to achieve The purpose of simplifying operation and maintenance procedures and saving investment.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".