Sensorless speed estimation for long term flywheel energy storage system in standby mode.
Bibliographic record
Abstract
A novel technique for sensorless speed estimation is presented in this thesis for squirrel cage induction machine (SCIM) drived long-term flywheel energy storage system (FESS) in standby mode. The SCIM model for long-term large-capacity FESS is presented. Based on dynamic model , a hybrid rotor flux observer and speed observer are derived. The hybrid rotor flux observer takes advantages of both the current model and voltage model flux observers by seamlessly incorporating these two models together for a better flux estimation performance even at low speed range. The fundamental speed observer is derived from the dynamic model for speed estimation with a fast response time for a tradeoff of the adaptive capabilities. In order to observe the speed in standby mode, a modified field-oriented control (FOC) scheme is presented. The hybrid flux observer and speed observer are tested in association with the modified FOC. The proposed control technique adopts approaches in an effort to minimize the impact generated by the excitation and speed estimation process to the FESS. Simulation and experiments are conducted to verify the feasibility of the proposed speed estimation at the standby mode. It is also observed that a step change of excitation current has a significant impact to the existing FESS. A ramp control for excitation current is added to avoid the possible oscillation of the estimated speed and the disturbance to the FESS. The speed estimation settling time is optimized based on the experiment and simulation.
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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 | 0.000 | 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".