Pseudo‐Measurement Models in Distribution Networks: A Review
Bibliographic record
Abstract
ABSTRACT To enhance stability and reliability of an electric distribution system, the monitoring and data acquisition through measurement devices, sensors and communication networks is essential to maintain the system observability for proper operation and control. However, insufficient measurement devices, and malfunctions of sensors and communication networks may lead to the monitoring data losses, one solution is to use the synthetic datasets, known as pseudo‐measurements, to substitute missing data and improve the system observability. Pseudo‐measurements are created through probabilistic, statistical and machine learning techniques using historical measurements of distribution systems. In this paper, potential roles of pseudo‐measurements to enhance monitoring of distribution networks have been reviewed. Two categories of pseudo‐measurement models are examined in this review: (1) probabilistic and statistical‐based models, including parametric, semiparametric and nonparametric approaches; and (2) machine learning‐based models, including shallow (conventional machine learning) and deep learning structures. Each model's computational demands and practical applications are analysed, highlighting their advantages and limitations. This review aims to identify the research gaps of pseudo‐measurement models and suggest future research directions for robust and adaptive monitoring of distribution networks.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".