Evaluation of wind capacity credit using discrete convolution considering the mechanical failure of wind turbines
Bibliographic record
Abstract
In view of the increasing role of wind power generation, there is an evolving body of reliability methods that are concerned with improved modeling of wind generation and related phenomena. An important consideration in the planning of wind generation projects is the capacity value of the farm at the proposed location. The modeling considerations in this process should take into account not only the variable nature of wind and the mechanical failure of turbines, but also the correlation between the individual turbines on the farm. This paper introduces an analytical method to calculate the capacity credit of wind farms including the mechanical failure of wind turbines. The proposed method is based on the discrete convolution technique and takes into account the stochastic nature of wind power as well as the forced outage rates (FOR) of wind turbines. The discrete convolution method has been used in this work to build a generation model in the form of a capacity outage probability table (COPT). A comparison of wind power capacity credit with and without considering the mechanical failures of wind turbines is shown to demonstrate the impact of turbine failure. Also, the capacity credit is calculated based on two reliability indices which are Loss of Load Expectation, LOLE, and Loss of Energy Expectation, LOEE. The proposed method is applied on the IEEE RTS-79 and the hourly wind speed data were taken from Abee Agdm Alberta, Canada. The results show the importance of inclusion of FOR of wind turbines on estimating wind power capacity credit. The results are validated using Monte Carlo simulation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".