Bibliographic record
Abstract
Abstract This article presents the basic concepts, topics, indices, outage models of system components, noncoherence phenomenon, and three specific issues in probabilistic system operation reliability assessment. Operation reliability of power systems includes adequacy and security evaluations for real‐time operation from a few minutes to half an hour and operation planning from half an hour up to 1 year. There are fundamental differences between reliability assessments for operation and long‐term system planning. The main features in operation reliability assessment are illustrated, and 26 topics in this area are proposed. The indices for system operation reliability can be classified into three categories: the indices of system operation states, limit violations, and system operation risks. The major challenges in system operation reliability assessment include probabilistic simulations of various operational measures, remedial actions, and system dynamics at different timescales, as well as special requirements in input data and computing speed. Fuzzy set theory can be combined with probability methods to deal with both randomness and fuzziness in time‐varying or condition‐dependent data modeling. The operation adequacy assessment for steady states and operation risk assessments for voltage and transient instability are discussed in detail as typical examples. Probabilistic reliability assessment of power system operation is an important task for power system researchers and engineers today and in the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".