Power system reliability evaluation using a state space classification technique and particle swarm optimisation search method
Bibliographic record
Abstract
It is well‐known that the reliability evaluation of composite power systems is computationally demanding. This work introduces a state space classification (SSC) technique that classifies a system's state space into failure, success, and unclassified subspaces without performing power flow analysis. The SSC technique was developed based on calculating the maximum capacity flow of the transmission lines and the available generation. An algorithm, which is developed based on a directed binary particle swarm optimisation, was developed to search for failure states in the unclassified subspaces. The key element in controlling the particle swarm optimisation (PSO) search method to search for failure states in the unclassified subspaces is the selection of the weighting factors of the velocity update rule. The work presented in this study proposes an intelligent PSO based search method to adjust these weighting factors in a dynamic fashion. The effectiveness of the proposed method was demonstrated on three test systems, the Institute of Electrical and Electronics Engineers reliability test system (IEEE RTS), the modified IEEE RTS and the Saskatchewan Power Corporation in Canada. The results have shown that the reliability indices obtained using the proposed method correspond closely with those obtained using Monte Carlo simulation with less computation burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| 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".