Data-Driven Based Low-Voltage Distribution System Transformer-Customer Relationship Identification
Bibliographic record
Abstract
Transformer-customer relationship identification refers to the determination of the physical connection relationship of electricity end-customers and their corresponding transformers. Such connection relationship is critical for distribution utilities to maintain their end-customer profiles. However, management of transformer-customer relationship becomes one of the most emerging challenges due to large number of end-customers and lack of measurement devices in low-voltage distribution systems. To address the above issue, this paper proposes an end-customer data-driven method to identify transformer-customer relationship in low-voltage distribution grid by utilizing the customer field data obtained from advanced metering infrastructure. Specifically, the incidence convolution identification method is proposed to build up the unique mapping relationship between end-customers and their transformers based on the principle of energy conservation. Then the voltage correlation maximization model based on Markov Random Field is proposed, where the voltage correlation matrix is exacted and combined with the adjacency matrix to establish an optimization model to correct the potential abnormal transformer-customer relationship. Finally, the effectiveness of the proposed method is verified by using practical utility tests.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".