Multi‐group particle swarm optimisation for transmission expansion planning solution based on LU decomposition
Bibliographic record
Abstract
As power systems are being highly stressed with the boost of loading levels and the introduction of new generation sources, transmission expansion planning (TEP) has regained its significance as a pivotal problem to be solved. To ameliorate the performance on both efficiency and accuracy for the solution of TEP from the aspect of algorithm design, a static DC TEP without generation redispatch is investigated by the proposed multi‐group particle swarm optimisation (MGPSO) algorithm. MGPSO is based on the discrete PSO framework with several beneficial enhancements involved, such as Sobol sequence initialisation method, multi‐group co‐evolution strategy, and mutation mechanism. For the solution of linear programming subproblem within the framework of MGPSO, a linear equation system is extracted and then addressed with efficient LU decomposition approach. Case studies have been implemented on five classical benchmarks, ranging from 6‐bus to 118‐bus, between the MGPSO and commercial software Lingo 11.0 to validate the superiority of MGPSO. Speedup analysis as well as performance evaluation of different acceleration strategy involved in MGPSO are implemented and discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".