Bibliographic record
Abstract
Harnessing green and renewable sources of energy is a future solution that addresses rising energy demands and growing environmental concerns. Among these, tapping wind energy using wind turbines appears to be one of the most promising solutions. A wind energy conversion system captures kinetic energy of wind and converts it into electrical energy. By nature, availability of wind energy is stochastic and intermittent. In contrast, electric power system expects a steady and planned supply of energy. This thesis addresses the gap in characteristics of wind energy supply and conventional electric energy demand. This thesis considers a doubly fed induction generator (DFIG) connected to a wind turbine to harness wind energy. The proposed topology connects a Supercapacitor through a buck-boost chopper to the DC link of rotor circuit. The Supercapacitor works to perform the job of a flywheel. The thesis proposes an appropriate control system that controls the output of the DFIG to constant value (Pref) eliminating short-term fluctuations. This control system works to control the buck-boost chopper and works as a inner control loop. Thereafter, this thesis proposes and optimization algorithm that considers short-term forecasted wind speeds (energy) for several minutes. It then optimizes to determine a minimum set of output values of the DFIG (Pref). It ensures that output of the DFIG has minimum changes thus minimizing intermittency in the DFIG output. This optimization algorithm forms the outer loop in the overall control strategy. The complete system is implemented in Matlab/Simulink and analysed in this thesis. The results demonstrate that the inner and outer control loops work to minimize output power oscillations and improve power quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".