Geographic information systems visualization of wind farm operational data to inform maintenance and planning discussions
Bibliographic record
Abstract
As utility scale wind farms age, maintenance and contingency planning become increasingly important. Decisions about when and how to repair or replace major turbine components can critically influence profitability. Condition monitoring and prognostic reliability modelling are sometimes used to support these decision-making processes. These often resource intensive, sophisticated techniques are frequently administered by third parties and can be black boxes to wind farm stakeholders. Early experience from the YR21 Investment Decision Support Program has highlighted the importance of broad engagement across wind farm teams in maintenance and planning discussions. The utilization of geographic information systems to illustrate data trends across wind farms proved to be a valuable tool in fostering fundamental understanding of an operation’s signature performance characteristics. This graphical representation of the farm provides a useful visualization of the operation’s best and worst performers in terms of power produced, wind speeds experienced, total revolutions, or highest gear box temperature. These transparent representations of the data represent valuable starting points for discussion of performance or potential maintenance issues across farms. In some cases, it can reveal unexpected trends that may raise bigger questions about how the farm is operating in general. Finally, these simple figures can serve as complementary inputs to larger, more complex data-driven decision systems. Geographic information system plots are presented for three wind farms to demonstrate the potential utility in simple, transparent, and accessible data visualization.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".