Event‐triggered load frequency control for multi‐area power systems based on Markov model: a global sliding mode control approach
Bibliographic record
Abstract
In this study, a new method is put forward for the stability and stabilisation analysis of the event‐triggered load frequency control (LFC) with interval time‐varying delays, considering the global sliding mode controller. To lighten the network bandwidth and save more limited networked resources, the event‐triggered scheme is optimised through quantum genetic algorithm, according to different circumstances. Additionally, global sliding mode control (GSMC) scheme is proposed to provide stronger robustness performance, which against the frequency deviation caused by power unbalance or transmission time delays better. Based on the proposed schemes, multi‐area LFC for the power system model is formulated as a Markov jump linear system model, considering transmission time delays and external disturbances. By applying improved Lyapunov stability theory, criteria about the stability and stabilisation conditions for multi‐area power system can be deduced in terms of linear matrix inequality. Finally, to validate a more realistic LFC application, the proposed event‐triggered GSMC is also deployed on Kundur's two‐area test system. Simulation studies are carried out to illustrate the effectiveness and superiority of the developed schemes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".