Diagnosing Fuel Pumps, Power Transducers, CTs, and PTs via Fuel-Power Function and 2oo3 Voting
Bibliographic record
Abstract
In power system operation, the actual operating cost of thermal generating units can be estimated by employing what is called a fuel-cost function. This function can be expressed as a linear, quadratic, or cubic equation. The unit operating cost is the dependent variable, while the independent variable can be the active or reactive power generated by that unit. The mathematical expression of this fuel-cost function is modeled by fitting a curve to match the actual unit readings. In regression analysis, it is known that if there is a relation between two variables, a transposed relation can also be created by making the predictor the subject of the formula instead of the response. In other words, the independent variable is taken as a dependent variable. This study aims to benefit from the fuel-cost function in estimating the unit power output. This means that a fuel-power function can be designed from the fuel-cost function. To validate this claim, a numerical experiment is carried out based on data collected from a real gas turbine (GT). A novel diagnosing system is proposed to check the status of fuel pumps, power transducers, current transformers, and potential transformers by merging the areas of power system automation, control, operation, and protection through a 2 out of 3 (2oo3) voting logic system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".