Algebraic Tracking Network Topology Processor for Modern Power Systems
Bibliographic record
Abstract
With the growing deployment of Phasor Measurement Units (PMUs) in Electric Power Systems (EPSs), status measurements from switchable devices are transmitted at high sampling rates, enabling rapid detection of switching events and continuous topology updates. Fast topology processing is therefore essential for reliable operation and decision-making in modern, dynamic EPSs. In this context, this paper proposes an Algebraic Tracking Network Topology Processor (AT-NTP), developed from algebraic formulations and a new islanding identification method. Using status measurements from PMUs and other sources, the AT-NTP determines and updates network topology through matrix factorization and refactorization, avoiding graph search algorithms and artificial intelligence techniques commonly used in existing topology processors. The AT-NTP is simple to implement, avoids combinatorial explosion, and does not require training stages. It efficiently detects switching events, island formation, bus merging or splitting, and measurement configuration changes without recomputing the topology from scratch. Its formulation applies to arbitrary substation configurations without requiring adaptations, making it flexible and suitable for various systems. Simulation results on benchmark and large-scale real networks demonstrate the AT-NTP’s computational efficiency and confirm its suitability for PMU-based state estimation and advanced energy management applications.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".