Partition of Large Power Networks Using a Metaheuristic Optimization Method
Bibliographic record
Abstract
Performing transient stability simulations for multiple contingencies for large interconnected power systems is computationally demanding. Parallel processing is the most obvious approach to speed up the simulation. One approach of allocating computational burden to multiple processors, is to divide the large network into several subnetworks. During each integration time step, sub-networks are solved independently on individual processors and voltage/current information is exchanged at the end of each time step. One challenge with this is the errors introduced due to time step delay. To minimize the source of errors, subdivision of the network must be done in such a way that the number of links between sub-networks is minimized. Furthermore, it is desirable to have weak links between the sub-networks. This paper proposes an application of Genetic Algorithm(GA) to divide the large networks into smaller sub-networks, minimizing the number of interconnections and giving preference to weak interconnections over strong interconnections. The IEEE 118 bus system is split into two equally sized sub-systems. The objective function considers the number of interconnections and number of strong interconnections.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 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".