Anomaly detection and treatment for meteorological and wind turbine power measurements
Bibliographic record
Abstract
A comprehensive Quality Assurance (QA) process was implemented on the meteorological measurements and power output of the neighboring wind turbine at the Wind Energy Institute of Canada's wind farm on Prince Edward Island. The data, obtained from May 2013 to September 2021, include wind power, speed, direction, temperature, pressure, and relative humidity, averaged over 10 min. The first QA step was the detection of erroneous values due to well-known causes, such as the icing of the cup anemometers, the wake of the neighboring turbine, and the meteorological mast. Subsequent, and mainly novel, procedures were developed to capture systematic errors, nonphysical values, and repeated constant values, to assess the internal consistency, and to deal with abnormally high variation and erroneous small values in the data. Most of the data taken before the calibration of the anemometers in 2017 and some a few months after the calibration were invalid; a fact that shows the importance of yearly calibration. The installation of two instruments on the opposite sides of the mast provided “counterpart” measurements, which greatly facilitated the QA. We also offer some recommendations about the layout of the mast and closest wind turbine and the value of using sonic anemometers which are not affected by icing.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".