Congestion Control and Optimal Size of a Photovoltaic Device Using Multiverse Optimization Technique
Bibliographic record
Abstract
Congestion management plays an important role in the operation, control, and safety of the grid. This paper proposes the multiverse optimization (MVO) algorithm for the congestion management of the IEEE 30 bus system, aiming to identify line congestion, and eliminate it at the minimum congestion price (i.e., the minimum loss). The continuation power flow (CPF) mechanism is adopted to analyze the voltage stability and maximum load capacity of the grid. The MVO algorithm helps to boost the voltage with a photovoltaic (PV) device, whenever the grid became unstable. The optimal position of the device is found through six iterations, and the fitness function is found capable of maximizing loading parameters, while minimizing power loss. The new approach is evaluated under different operating conditions, namely, in the presence of an MVO-tuned PV grid, and in the absence of a PV grid. The results show that the MVO-tuned PV grid performed much better than the grid without a PV.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".