Multi-timescale Electricity Theft Detection and Localization in Distribution Systems Based on State Estimation and PMU Measurements
Bibliographic record
Abstract
Electricity theft is a serious issue for distribution companies around the world. Often linked to criminal activities, it is dangerous for the grid and the neighborhoods. While placing measurement points at each bus would allow an easy detection, it is not a practical approach. In this paper, a multi-timescale theft estimation (MISTE) method that takes advantage of smart-meters as well as the sparse grid sensing infrastructure that is being envisaged for state estimation is proposed. It combines power and voltage measurement across time to detect any inconsistency caused by electricity theft. Contrary to existing approaches which are snapshot-based and assume smart-meters to be able to measure instantaneous power consumption, the proposed method models smart-meters as energy measurement devices and combines the measurement timescales of the smart-meters and the PMUs in the computations. The detection performance of the proposed approach is compared to the state of the art theft detection methods. Both the true positive rate as well as the false negative rate are considered, which few papers have discussed previously. Insights on the impact of theft location on theft detection are also given.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".