Design and Techno-Economical Optimization of Wind Turbine Generator System: Under Diverse Metrological Conditions
Bibliographic record
Abstract
The Kenyan Electricity Supply Industry (KESI) generation mix comprises; hydro-electric, geothermal, thermal and a small contribution from wind energy. Over the years, the predominant source of electricity generation in KESI has been hydro (over 60%). However, due to climate changes in recent times hydro generation has become less reliable and largely contributed to the supply–demand mismatch. Against this background there has been a paradigm shift in energy policy towards the use of Renewable Energy sources for electricity generation. Informed by this shift of policy, this paper explores the possibility of generating electrical power from wind-at Mokowe, Lamu County, in Kenya along the Indian Ocean. The paper contends that the use of optimally designed Wind Turbine Generators (WTGs) could enhance production of electrical power at the proposed site. This in effect would significantly: improve the technical and economic performance of KESI; and impact positively on the socio-economic status of the local communities at Makowe.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".