Distribution System State Estimation Based on Nonsynchronized Smart Meters
Bibliographic record
Abstract
Distribution systems are undergoing many enhancements and developments to enable the future smart grid, and distribution system state estimation (DSSE) provides the control centers with the information necessary for several of its applications and operational functions. However, the quality of DSSE typically suffers from a lack of adequate/accurate measurements. Recently, many electric utilities have started to install fairly accurate smart meters throughout their distribution networks, which create an opportunity to achieve higher quality DSSE. However, the signals provided by smart meters are generally not synchronized and the difference between the measurement times of smart meters can be significant. Therefore, a complete snapshot of the entire distribution system may not be available. This paper proposes a method to deal with the issue of nonsynchronized measurements coming from smart meters based on the credibility of each available measurement and appropriately adjusting the variance of the measurement devices. To illustrate the effectiveness of the proposed method, two IEEE benchmark systems are used. The results show that the proposed method is robust and improves the accuracy of DSSE compared with the traditional DSSE approach.
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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 | 0.003 |
| 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 | 0.001 | 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".