Eco-Friendly Selection of Diesel Generator Based on Genset-Synchro Technology for Off-Grid Remote Area Application in the North of Quebec
Bibliographic record
Abstract
For most of their energy requirements, greater part of remote communities and small islands around the world rely on imported fossil fuels. The economical cost of energy is therefore very high not only due to inherent cost of fuel, but also due to transportation and due to maintenance costs. One solution for saving fuel in a diesel generator is to allow the engine to operate directly in relation to the request for electrical load at variable speeds. Genset-Synchro Technology has developed an innovative variable speed generator technology (patent pending) that allows applications where power demand varies widely to benefit from the new technology that maintains constant voltage and frequency while adjusting the generator stator speed to power demand. This paper will present an innovative approach for optimizing the energy production based from the fact that the structure that contains the stator windings of the generator is mounted on roller bearings, which allows its free rotation around the axis of the rotor, consequently stopping the stator structure from being static and aims to minimize the unit cost of electricity. Case study on application in remote area in the north of Quebec is described. A saving of 7% - 9% on fuel consumption and greenhouse gas (GHG) under low winter ambient temperatures has been registered.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".