Rule-based data-driven analytics for Wide-Area fault detection using synchrophasor data
Bibliographic record
Abstract
Synchrophasor technology, also known as Wide-Area Monitoring System (WAMS) technology, utilizes Phasor Measurement Unit (PMU) to monitor real-time system data, which can provide unique insights into the operation of a power grid. In this paper, a rule-based data-driven analytics method for wide-area fault detection in a power system using synchrophasor data is proposed. As a data-driven approach, this method relies on rules created using PMU measurement data, and does not require knowledge of the power system's topology and model. It can detect fault location (bus and line) and fault type for a particular fault event. Three common types of short circuit faults in a power grid, single-line-to-ground (SLG), line-to-line (LL), and three phase faults, can be identified using the proposed method. Fault thresholds used in rules are determined based on theoretical values and recorded PMU data during fault events in Bonneville Power Administration (BPA)'s large power grid. The proposed method is validated by comparing with the recorded field data for fault events provided by BPA. It is found that it can effectively detect most faults with a great accuracy. It has been developed into a software program, and can be readily used by utility companies.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".