Distributed computing approach to solve unbalanced three-phase DOPFs
Bibliographic record
Abstract
Distribution systems have been gradually improved with new technologies. They have been upgraded from the traditional system with low-level control to a smart-grid system with high-level control. In the present work, a mathematical model of an unbalanced three-phase distribution system, including ZIP loads and other components of distribution systems is used, and a Genetic Algorithm (GA) -based Distribution Optimal Power Flow (DOPF) model is applied to find the optimal integer solutions for discrete system control elements such as Load Tap Changers (LTCs) and Switched Capacitors (SCs) in a practical feeder. In order to reduce the computational burden and consequently the run-time, a communication Middleware System for smart grids is used to solve the GA-based DOPF problem on a decentralized computer system using a parallel computing approach. This system is responsible for running the model, managing all communication between the nodes, and transferring the results between various parts of the parallel system. Comparing with heuristic methods with faster sub-optimal solutions in a centralized computer system, the present work is expected to yield better optimal solution within acceptable practical run-times.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".