Electromagnetic modeling of large hydro electrical generators using 2D finite element method
Bibliographic record
Abstract
With the ever increasing of the demand for electrical energy, the need for additional power has already become a reality in the energy supply market. In this context, increasing the power output of existing generators is considered as a promising and beneficial option. Unfortunately, the available methods today do not allow exploiting the full power potential of the existing generators with a precise evaluation of the impact of this increase on the residual lifetime. The challenge in uprating a large existing machine therefore is increasing the output power within the safety limits and without reducing the reliability of the system. One of the goals of Hydro-Québec is to develop new techniques which permit identification of units with potential for up rate. The understanding of the electromagnetic fields inside the generator and their implication in the losses is the first step to fulfil this goal. This thesis focuses on analyzing the electromagnetic field and establishing the magnetic core losses for different machine configurations and operating conditions. The electrical machine in question is a low-speed, salient poles synchronous generator. Initially, a literature review of the related subjects published in the last 20 years was investigated. Secondly, modelling analysis and simulation of large electrical machines using commercial software to compute the losses at the steady state was also done. Moreover, validation of this work and analysis is done using measurements of losses with emphasis on the core losses and magnetic flux density. The core loss matrix generated by FEM was also compared with the thermal calculation and measurements of the hotspots of the machine. The results from FEM simulation in comparison to the experimental measurements are presented for two machines (Manic 21 and Rapide-des-Quinze). The simulation and experimental results were in a good agreement.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".