Power Distribution Network Topology Detection Using Dual-Graph Structure Graph Neural Network Model
Bibliographic record
Abstract
Topology detection (TD) in the context of power distribution networks (PDNs) is a fundamental requirement for a wide range of applications, such as fault localization and load management. PDNs suffer from a lack of real-time topological information due to insufficient data on switch statuses and an increasing number of switching actions caused by reconfigurations and the control of distributed energy resources (DERs). On this basis, in this paper, a novel near real-time TD method for PDNs is proposed. This method is built on a specialized graph neural network (GNN) design using data from micro-phasor measurement units ($\mu $PMUs), leveraging the strengths of both graph-based learning and conventional deep learning (DL) approaches. More specifically, the developed TD method implements a novel dual-graph structure GNN (DGS-GNN) model to transform the TD problem into an inductive link prediction task for a multi-graph dataset. During the training phase, a node attribute similarity graph is created, and the resulting node embeddings are aligned with the actual topology graph (ATG) using a structure-aware loss function. In the inference phase, however, unlike standard GNN models that require structural information as input, the ATG is recovered based solely on node attributes. The developed method enables TD using a limited number of phasor measurements with low inference time and superior generalization capability for unseen scenarios. Its strong performance in large-scale PDNs with varying configurations, as well as its robustness to uncertainties from DERs and noisy environments, is demonstrated on the IEEE 33- and 123-Bus benchmarks and a standard 240-Bus test system. The proposed method outperforms its DL-based counterparts in scenarios where full or partial system topology should be detected.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".