A Simplified Wind Power Generation Model for Reliability Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Renewable energy sources, especially wind turbine generators, are considered as important generation alternatives in electric power systems due to their nonexhausted nature and benign environmental effects. The fact that wind power penetration continues to increase has motivated a need to develop more widely applicable methodologies for evaluating the actual benefits of adding wind turbines to conventional generating systems. Reliability evaluation of generating systems with wind energy sources is a complex process. It requires an accurate wind speed forecasting technique for the wind farm site. The method requires historical wind speed data collected over many years for the wind farm location to determine the necessary parameters of the wind speed models for the particular site. The evaluation process should also accurately model the intermittent nature of power output from the wind farm. A sequential Monte Carlo simulation or a multistate wind farm representation approach is often used. This paper presents a simplified method for reliability evaluation of power systems with wind power. The development of a common wind speed model applicable to multiple wind farm locations is presented and illustrated with an example. The method is further simplified by determining the minimum multistate representation for a wind farm generation model in reliability evaluation. The paper presents a six-step common wind speed model applicable to multiple geographic locations and adequate for reliability evaluation of power systems containing significant wind penetration. Case studies on a test system are presented using wind data from Canadian geographic locations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it