Variable Speed Diesel Electric Generators: Technologies, Benefits, Limitations, Impact on Greenhouse Gases Emissions and Fuel Efficiency
Bibliographic record
Abstract
A substantial share of the electric energy is generated with synchronous generators that provide sustained alternating current (AC) voltage and frequency energy to regional and national power systems, which subsequently transport and distribute it to diverse users. In an attempt to reduce environmental effects, electric energy markets have recently become more open, resulting in more flexible distributed electric power systems. In such distributed systems, stability, quick and efficient delivery, and control of electric power require some degree of power electronics control to allow for lower power in the electric generators to tap the primary fuel energy potential better and increase efficiency and stability. This is how variable-speed electric generators (VSEG) recently came into play, up to the 400-megavolt ampere (MVA)/ unit size, and which have been at work since 1996. This paper provides coverage of variable-speed electric diesel generators (VSDEG) in distributed generation and their impacts on fuel efficiency and greenhouse gases (GHG). It discusses permanent-magnet-(PM) synchronous generators, solutions based on power electronics such as diesel-driven wound-rotor-induction generator, doubly-fed-induction generator (DFIG), rotating stator generator, and the application of continuously variable transmission to a VSEG. The benefits and limitations of the selected technologies are also presented. The list of references given at the end of the paper should offer aids for students and researchers working in this field.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".