Spectral clustering for designing robust and reliable multi‐MG smart distribution systems
Bibliographic record
Abstract
Reliability has become a key design aspect in modern energy system's planning. Owing to the higher fault rate in power distribution systems (PDSs), comparing with generation and transmission systems, considering reliability in PDSs’ planning is very crucial. This study presents a novel robust approach to cluster the existing PDSs with intermittent distributed generators (DGs) into a set of reliable microgrids (MGs). For this purpose, first, a new reliability index is defined to evaluate the reliability of MGs in terms of real and reactive power adequacy as well as frequency and duration of interruptions. Then, the k ‐means algorithm, based on weighted graph partitioning method, is proposed for changing the system into a multi‐MG system. Furthermore, a modified version of particle swarm optimisation approach is proposed and the Silhouette technique is used to determine the optimal location and sizes of DGs as well as the number of MGs. The design and sensitivity analysis performed by the proposed multi‐objective optimisation algorithm on the well known IEEE 69‐bus distribution system show the effectiveness and robustness of the proposed algorithms for constructing reliable MGs in modern PDSs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".