Sizing and control of flywheel energy storage for Ramea wind-hydrogen-diesel hybrid power system
Bibliographic record
Abstract
In this thesis, a flywheel energy storage system (FESS) is proposed for the isolated wind-hydrogen-diesel hybrid power system in Ramea, Newfoundland, Canada. Sizing and control of the flywheel energy storage for Ramea wind-hydrogen-diesel hybrid power system is presented. At present, the hybrid power system in Ramea consists of diesel generators, wind turbines, hydrogen generator and Electrolyzer. The main objective of this proposed flywheel energy storage system is to minimize sudden variations of grid voltage and frequency due to variable wind speed and variable load. HOMER software is used for sizing and steady-state simulations, while MATLAB/Simulink is used for modeling and dynamic analysis of Ramea Hybrid power system. The selected size of the FESS is tested experimentally using a setup which is composed of 2kW DC machine based flywheel energy storage. The test DC machine is used as a motor/generator unit coupled with a cast steel flywheel rotor, a programmable data acquisition card, two controllable power supply units, two electromechanical relays and relay driving circuits. Steady state simulation shows that the addition of a flywheel energy storage to Ramea system reduces the fuel consumption by the diesel generator, reduces the excess electrical energy per year, increases the renewable penetration to the grid and reduces the number of starting the diesel generator per year. These observations can be advantageous in prolonging the life span of the diesel generator, as well as reducing its maintenance. Also the observations indicate that the voltage on the grid-side can stabilized under frequency dynamic variation.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".