PMU-Based Distribution Linear State Estimation to Improve Data Quality and Application Reliability
Bibliographic record
Abstract
Synchrophasor data quality issues have been a challenge for robust application deployment in the power grid. This paper presents the ability of a three-phase distribution linear state estimator (DLSE) to detect various types of synchrophasor bad data. The DLSE is the first application layer that pre-processes the synchrophasor data and produces state estimates for all observable nodes of the system. This work represents the DLSE software for real-time monitoring, analysis, and situational awareness of the Commonwealth Edison Company (ComEd) Bronzeville Community Microgrid (BCM). The DLSE software platform, used for state estimation, can perform observability analysis, optimize distribution phasor measurement unit (PMU) placement, identify topology change, and detect bad data. This paper focusses on the software's module that supports bad data detection. Demonstration of the PMU-based platform was performed in ComEd's real-time digital simulation (RTDS) enabled Hardware in the Loop (HIL) environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".