A Wavelet/PSO Based Voltage Regulation Scheme and Suitability Analysis of Copper- and Aluminum-Rotor Induction Machines for Distributed Wind Power Generation
Bibliographic record
Abstract
Centralized generation is being supplemented or replaced fast by distributed generation, a new way of thinking about electricity generation, transmission and distribution. Understanding the significance and prospects of self-excited induction generators (SEIGs) in distributed wind power generation (DWPG), this paper firstly presents a comprehensive suitability analysis of commercially available niche copper-rotor induction motor (CRIM) and conventional aluminum-rotor induction motor (ARIM) to be used as induction generators in the above application, through experimental investigations performed on two industrial 7.5 hp CRIM and ARIM. The results of the performance analyses on the two machines provide data for development of a novel control scheme comprising of wavelet transforms and particle swarm optimization technique, proposed next, to alleviate the problem of voltage regulation (VR) associated with the SEIGs. The developed controller for VR is implemented on a low cost embedded system, hence making it economical and flexible for patenting. The developed embedded system is validated initially through an actuator performing mechanical switching of requisite capacitances and finally by integrating it with another actuator, namely, the static synchronous compensator (STATCOM), for efficient voltage regulation. Finally, the tested results are presented.
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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".