Performance Optimization of Diesel Generators Using Permanent Magnet Synchronous Generator with Rotating Stator
Bibliographic record
Abstract
One of the solutions to reduce fuel consumption of diesel generators (DG) is to adapt the rotational speed to mechanical torque of the crankshaft. When load power decreases, a reduction in both mechanical torque and rotational speed of the diesel engine will maintain the combustion efficiency near the levels of the nominal regime. Accordingly, the generator itself should operate at a variable speed which normally requires power electronics converters. In this paper, we are exploring a new generator concept that uses a stator rotating in opposite direction to the rotor such as the relative velocity between the two components remains constant when diesel engine slows down. The stator itself is driven by a compensator synchronous motor (CM) such as the relative velocity of the rotor is constant, eliminating as such sophisticated power electronics. The model developed for the synchronous machine with a rotating stator is based on Park’s transformation. This new concept was modelled using MATLAB software. Experimental analysis has been conducted using a 500-kW diesel GENSET equipped with a permanent magnet synchronous generator (PMSG). The numerical and experimental results are in good agreement and demonstrate that fuel consumption is reduced with a rotating-mode stator for PMSG during low electrical loads.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".