Smart distribution system volt/VAR control using distributed intelligence and wireless communication
Bibliographic record
Abstract
This study presents a smart volt/VAR control (VVC) technique for smart distribution systems, which is designed to be integrated into a distributed real‐time analysis and control framework implemented with the use of multiple processing units equipped with wireless communication transceivers. The distributed processing units collaborate to perform power flow analysis based on smart meter measurements for controlling and coordinating the switched capacitor banks and voltage‐regulating transformers. The objective is to maintain acceptable voltage levels along the distribution feeder, minimise system losses, and limit the number of switching operations. GNU‐Octave simulations are employed as a means of evaluating the performance of the proposed smart VVC technique with respect to loss reduction and number of switching operations. The network simulator ns‐3 is used to simulate the distributed processing units that execute the proposed smart VVC technique. Worldwide interoperability for microwave access (WiMAX) and long‐term evolution (LTE) communication networks are employed in the ns‐3 simulations in order to provide data connectivity among the distributed processing units. The performance of the communication network is evaluated in terms of the execution time of the smart VVC technique, the average packet delay and the average packet delivery ratio.
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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 | 0.000 |
| 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) | 0.001 | 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".