Optimal placement of resistive/inductive SFCLs considering short‐circuit levels using complex artificial bee colony algorithm
Bibliographic record
Abstract
In mature electric power systems, growth in generation/demand, integration of renewable energy, and system expansion may elevate short‐circuit levels beyond the rating of existing components. Thanks to technological advancements in materials, superconducting fault current limiters (SFCLs) can effectively alleviate excessive fault currents without affecting normal operation of power systems as they are invisible in non‐faulted conditions. However, due to their rather high prices, SFCL optimal placement (SOP) comes to attention. The effectiveness of SOP depends on optimally siting of SFCL resistive/inductive types, which vary in transmission and distribution networks due to different X/R ratios. In this study, an SOP is proposed to determine optimal locations and types of SFCLs taking into account short‐circuit level of buses. In addition, a complex‐valued artificial bee colony (CABC) algorithm is introduced to efficiently solve complex‐valued optimisation problems such as power system applications, including SOP. The proposed SOP with CABC is examined on transmission and distribution test cases to evaluate its effectiveness. It is found that by employing the proposed complex decision vector, the CABC algorithm exhibits an enhanced exploration capability and convergence rate due to halving decision vector length and considering mutual effects of real and imaginary parts of decision variables.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".