Using distributed intelligence and wireless communication to control and coordinate multiple capacitor banks
Bibliographic record
Abstract
Switched shunt capacitor banks are commonly used in distribution networks to reduce the system losses and support the voltage. In this paper, distributed processing units equipped with wireless communication transceivers are installed on different buses (nodes) of distribution system, and are used to control and coordinate multiple capacitor banks in order to maximize the distribution system loss reduction. Real-time information from the smart meters, e.g. measured active and reactive load powers, are utilized by the distributed processing units to perform real-time distributed load flow analysis. The resulting voltage profile and current flow in each branch are used to iteratively compute the switched capacitor banks states that maximize the system loss reduction. The Network Simulator NS-3 is used to co-simulate the proposed capacitor banks control scheme along with the wireless communication network. The distributed load flow analysis and the the proposed control scheme are implemented as C++ applications within the network simulator NS-3, and a WiMAX wireless communication network is used in the co-simulation. The percentile rank of the computed capacitor banks state, which is obtained from NS-3 co-simulation, is used to compare the performance of the proposed control and coordination algorithm to the optimal state which obtained from GNU-Octave exhaustive search.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".