A New Method to Monitor the Primary Neutral Integrity in Multi-Grounded Neutral Systems
Bibliographic record
Abstract
In the three-phase four-wire system, there are usually multiple grounding points in the primary neutral line due to safety and economic considerations. The rising “neutral to earth voltage (NEV)” caused by a broken primary neutral can threaten the safety of nearby facilities and humans; therefore, the integrity of the primary neutral conductor is of vital importance for the multi-grounded neutral (MGN) system. In this paper, a new passive method is proposed to monitor the integrity of the primary neutral line in the MGN system. The method is based on the measured voltage and current data at the service transformer, and there is no need to install any signal generators. Therefore, it causes no disturbance to the utility and customer. In the paper, the equivalent analysis circuit is established and a new parameter is proposed to reflect the neutral condition. The value of the parameter is estimated based on the measured data, and then, the equivalent impedance of the primary neutral groundings can be obtained. On the other hand, the impedance value for the primary neutral under normal operating conditions can be estimated based on the derived analytical formulas. By comparing the monitored primary neutral impedance with its normal value, the broken neutral condition in the primary system can be detected. Different primary neutral broken cases are analyzed in the paper based on the Monte Carlo simulation. The results indicate that the integrity condition in the primary neutral can be accurately monitored by 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".