Topology and Parameter Identification in Electrical Distribution Systems using Spatial Priors
Bibliographic record
Abstract
This manuscript presents novel methods that allow the consideration of spatial priors derived from Geographic Information Systems (GIS) for System Identification (SI), i.e., Topology Identification (TI) and Parameter Identification (PI), in electrical distribution systems. The proposed methods are designed to allow flexibility in the assumed measurement devices and the integration of micro-Phasor Measurement Units (µPMU) and Non-Phasor Measurement Units (NPMU), based on power flow approximations and GIS data associated with the location of measurement devices to deduct topological priors and cable and line parameter ranges based on spatial relationships between measurements. Based on a Mixed-Integer Quadratic Programming (MIQP) optimization problem, the proposed SI approach can handle measurement errors and noise. The presented method is demonstrated on a benchmark 17-node Low Voltage (LV) grid for three different scenarios, analyzing errors with respect to topology and parameters, as well as the computational effort. It is shown that by using spatial priors, the proposed SI method performs better than existing techniques.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".