Low Voltage Distribution Network TN-S Impedance Identification Considering Single and Three-Phase Hybrid Connection
Bibliographic record
Abstract
The low-voltage distribution network (LVDN) terrestrial-neutral-separated (TN-S) impedance refers to the nodal resistance and reactance values on a three-phase five-wire line. The distributed photovoltaics and electric vehicles are increasingly integrated into the LVDN in either single-phase or three-phase mode. It will aggravate the three-phase unbalance, produce unbalance current in the neutral line, which brings difficulties to phase decoupling for impedance estimation. In this regard, this article proposes a LVDN TN-S impedance identification method that considers the single-phase and three-phase hybrid connection. Specifically, a LVDN line impedance model containing the neutral and phase line coupling relationship is proposed. Subsequently, the self-impedance of the phase and neutral line is modeled using a hierarchical-multiple regression, and the mutual impedance estimation model is proposed to investigate its impact on the TN-S line impedance. Then, a joint cross-iterative algorithm is devised to improve the accuracy of impedance identification. Finally, the effectiveness and accuracy of the proposed method are verified through a simulated 30-node LVDN test and an actual community system.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".