Reweighted Compressed Sensing-Based Smart Grids Topology Reconstruction With Application to Identification of Power Line Outage
Bibliographic record
Abstract
Smart grid (SG) can automatically collect a large amount of data of different power parameters through different sensors, which has become the future trend of power systems, especially for real-time monitoring needs. However, due to the limitation of detection technology and measurement cost, direct measurement of SG topology is difficult. Thus, reconstructing the topology of SG through measurement data is important yet challenging. In this article, we leverage a graph theory-based power network model and propose to transform the SG topology reconstruction (SGTR) problem into a sparse recovery problem. By exploiting the framework of compressed sensing, the network structure can be reconstructed from a small number of observations. In order to enhance the reconstruction performance, we extract three underlying features of the SG, namely, the symmetry feature, diagonal feature, and cluster feature. Thus, the symmetric reweighting of modified clustered orthogonal matching pursuit method was proposed to integrate these three features simultaneously to improve the performance of SGTR. In order to verify the efficiency of the proposed method, we conduct extensive experiments based on the MATPOWER benchmark toolbox. Compared with some state-of-the-art methods, we find that the reconstruction performance of the proposed method is obviously improved. In addition, based on the reconstructed SG topology, a sparse solution based on QR decomposition was also proposed to locate the power line outages accurately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".