An analytical method for wind energy potential, reliability, and cost assessment for wind generation systems
Bibliographic record
Abstract
In this paper, a new analytical method and a corresponding two-step procedure are proposed for a wind power generation system design. This comprehensive new method can achieve the wind energy potential evaluation, reliability and costs assessment. In Step 1, the wind energy potential is investigated through the Weibull two-parameter model using the hourly wind speed data of a site. The graphic method is used to extract the Weibull shape and scale parameters. The wind power density can then be determined for the site. In Step 2, an analytical method based on the fault tree analysis (FTA) and minimal cut sets is developed in order to determine the system reliability. The components' failure rates of a doubly fed induction generator (DFIG) wind turbine are used to calculate reliability. A generic annual operation and maintenance (O&M) costs calculation formula is proposed in this paper based on field data presented by National Renewable Energy Laboratory (NREL). A case study is conducted for a wind power project in St. John's, Newfoundland and Labrador, Canada. The proposed method is critical for planners and financial investigators to make an adequate decision for a wind power project.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".