Network-enabled Real-time monitoring and controls of smart power distribution systems
Bibliographic record
Abstract
Distributed generations (DGs) powered by renewable energy resources have become increasingly popular and starts to show benefits, but their connection to distribution systems brings operation challenges that must be carefully monitored and controlled. This paper proposes a new network-enabled, real-time monitoring strategy for tracking the operating states of the distribution system, with a focus on monitoring the dynamic operations of DGs. The design of a new network monitoring architecture is presented in this paper. This architecture is fault tolerant and has features from classical cascading, star, and ring architectures. The paper presents the real-time data acquisition and data post-processing strategy, design, and implementation in three layers: cell units for monitoring feeder-tapped circuits including DG circuits connected on the feeders, node unit for feeder and substation-layer equipment, and station unit for distribution network. Case studies are provided to demonstrate the proposed monitoring strategy and architecture using IEEE feeder circuits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".