A sequential simulation method for the generating capacity adequacy evaluation of small stand-alone wind energy conversion systems
Bibliographic record
Abstract
This paper describes a sequential Monte Carlo simulation method for generating capacity adequacy evaluation of small stand-alone wind energy conversion systems containing battery storage. The wind speed, the energy conversion by the wind turbine generator, the equipment reliability and the energy storage facilities are major factors influencing the reliability performance of a wind energy conversion system. Time series models were used to simulate wind speeds incorporating any necessary chronological correlations. The power available from a wind turbine generator was calculated from the simulated wind speed using the function describing the relationship between wind speed and power output. The failure and repair characteristics of a wind turbine generator were simulated in a similar manner to those of conventional generating units. A battery state of charge time series was obtained from the load time series and the available wind generation time series. The performance of such a system is quite different from one containing conventional generating units due to the dispersed nature of the wind at the specific site location. The results and the discussions presented in this paper should prove useful in planning, designing, and operating small stand-alone wind energy conversion systems for electricity supply in remote areas.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".