A Physics‐Data Driven Approach for Identifying Leakage Users in Low‐Voltage Distribution Systems
Bibliographic record
Abstract
ABSTRACT Wiring errors, caused by improper connections between neutral lines and protective earth (PE) lines due to negligence by electrical technicians, are a prevalent type of earth fault in low‐voltage distribution systems (LVDS). These errors can cause the load current of affected users to flow back to the transformer's neutral point via the PE line as residual current, leading to nuisance tripping of residual current devices (RCDs). To maintain normal power supply, users may disable RCDs, which compromises safety and can result in severe hazards such as electric shocks and electrical fires. This paper proposes a method to locate users with wiring errors by leveraging abundant metering data within LVDS and utilising leakage fault analysis devices. We construct a linear model of residual current considering multiple error scenarios. Based on this model, a multiple linear regression (MLR) approach is developed to identify and locate anomalous users by analysing the correlation between their load currents and the residual current of the LVDS. Experimental results under various scenarios validate the performance of the proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".