BC hydro experiences with utilization of pseudo measurements in state estimation
Bibliographic record
Abstract
State estimator application is the core advanced application in the Energy Management system (EMS) that provides major inputs to other network applications that are executed to determine power system security in the real-time. Those applications include transient and voltage stability analysis that are also responsible for calculation and download of the remedial action schemes arming patterns to the field in the real-time. For this reason, state estimator performance and quality of results are highly important to BC Hydro real-time operations. State estimator relies on the quality of status and analog real-time telemetry and is also strongly dependent on the availability of measurements to provide observability and redundancy. In practical world, real-time measurements are seldom available at all locations and in sufficient quantity to ensure observability of the entire transmission network. In order to overcome the lack of real-time telemetry state estimators traditionally rely on utilization of pseudo measurements to complement real-time measurement set and provide necessary observability for state estimator to solve. The objective of this paper is to discuss the approaches that BC Hydro has adopted for application of pseudo measurements as well as methods used to increase quality of pseudo telemetry.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".