Kalman Filter Based Predictive Trip Detection for Nuclear Power Plant Safety Systems
Bibliographic record
Abstract
The primary aim of this work is to utilize a Kalman filter (KF) to predict reaching the trip set-point for a trip parameter in a nuclear power plant (NPP). To address uncertainty in the predicted measurements, prediction bounds are calculated by propagating the state error covariance. These predicted bounds enable the calculation of levels of confidence in making trip decisions. Further, to address uncertainty in the estimation model, the observed prediction error is used to offset the predicted measurements. The predictive trip detection routines are evaluated through simulations of a single NPP sub-system. More specifically, the water level parameter in a steam generator of a NPP is considered. The model of this sub-system is represented by the Irving linear parameter varying (LPV) model with inverse response characteristics. The simulations include a level low postulated initiating event (PIE) made to occur during two different common power transients for various estimation models. The results of this paper are a proof of concept for KF-based predictive trip detection which is demonstrated to achieve reduced time-to-trip when applied to a single sub-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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".