Data-Driven Based Low-Voltage Distribution System Transformer-Customer Relationship Identification
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it