Design and Experimental Validation of a New Outer Rotor Double PM Excited Flux Switching Generator for Direct Drive Wind Turbines
Bibliographic record
Abstract
An outer rotor double permanent magnet (PM) excited flux switching generator is designed and optimized in this paper for direct drive wind turbine applications. This generator consists of two sets of PMs: ferrite PMs embedded in the stator yoke and neodymium PMs sandwiched between the rotor segments. In this regard, the main justification for employing ferrite PMs in the stator yoke is that the risk of demagnetization of ferrite PMs at high temperatures is lower than that of neodymium PMs (the temperature of the machine’s stationary parts is higher than that of its rotating parts). For the design of the machine, the Taguchi design of experiments is deployed, while a decision-making algorithm based on the technique for order of preference by similarity to the ideal solution is used to solve the contradiction that results from the Taguchi design of experiments in the multi-objective design optimization process. During the multi-objective design optimization steps, simultaneously maximizing the no-load phase voltage and minimizing the cogging torque and total harmonic distortion of the no-load phase voltage are defined as the objective functions. The optimally designed machine is prototyped and subsequently subjected to experimental validation to verify the predictions in satisfying the objective functions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".