Design methodology to optimise induction machines based stand‐alone electrical wind water pumping systems
Bibliographic record
Abstract
A novel methodology for designing a wind‐electric water pumping system is developed in this study. The system employs a self‐excited induction generator (SEIG) driven by a wind turbine (WT) and an induction motor (IM) feeding a water pump. Selection of values and proper configuration of excitation capacitors for this system is the key factor which is performed by an optimisation algorithm. The methodology commences with the design of hydraulic system and choice of proper pump. Then, the gearbox ratio of the chosen WT is determined to coordinate the characteristic of the WT with that of the pump. The aim is operation of the system near the maximum power output of the WT in different wind speeds. Thereafter, the electrical system is designed by choosing the suitably rated powers for the SEIG and IM. The optimal capacitor values in various configurations, viz. the shunt, short shunt, long shunt, T, and Π, are calculated utilising the genetic algorithm and then, the best configuration considering the system operating conditions is introduced. The objective function is defined to regulate the IM operating point at the knee point of its magnetising characteristic under wind speed variations. The system performance is evaluated through simulation and experiment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".