GPU‐based parallel real‐time volt/var optimisation for distribution network considering distributed generators
Bibliographic record
Abstract
Although the wide integration of advanced metering infrastructure on distribution network facilitates the application of volt/var optimisation (VVO) in real‐time circumstance, the contradiction between heavy computation load and low solution efficiency is still a big challenge, thus the system scales investigated in the literature are limited. In this study, the full AC real‐time VVO is formulated based on particle swarm optimisation (PSO) framework and direct approach (DA) power flow method, where all components, such as distributed generator and on‐load tap changer transformer, are formulated and integrated into the iterative DA process. Since both PSO and DA are suitable for parallel implementation, the graphics processing unit (GPU) is introduced for acceleration in order to achieve the possibility for real‐time application. All the solution process is executed by GPU with the well‐established data structure and thread organisation pattern, resulting in high efficiency by guaranteeing coalesced access within each warp. Case studies are conducted on four systems with sizes ranging from 136‐bus to 1760‐bus. Solution accuracy and convergence property are validated by the popular open source package Matpower. Based on the results from solution efficiency comparison between CPU sequential, CPU parallel, and GPU parallel programs, the promise of the proposed parallel implementation scheme for practical application is established.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".