Intelligent event trigger based sliding mode control in a marine current turbine with superconducting magnetic energy storage
Bibliographic record
Abstract
Marine current turbines (MCTs) are a burgeoning renewable energy technology that may effectively capture the kinetic energy of ocean currents to produce power. Nevertheless, the sporadic and uncertain characteristics of marine currents present substantial obstacles to the reliable functioning of grid-connected MCT systems. By incorporating Superconducting Magnetic Energy Storage (SMES) into grid-connected marine current turbines and implementing intelligent event-triggered Sliding Mode Control (ETSMC), we can significantly improve the transient voltage stability of marine renewable energy systems. The sophisticated event trigger mechanism continuously checks the circumstances of the grid and the operation of the turbine in real-time. The real-time nonlinear control technique enhances the performance of SMES by effectively regulating the flow of active and reactive power, hence ensuring grid stability during transient occurrences. This integrated system aims to improve the dependability and effectiveness of marine current turbine operations, thereby supporting the progress of sustainable marine renewable energy technologies. The resilience of the system was evaluated by its implementation in real-time on a dSPACE-DS1104 board, which was connected to an experimental laboratory bench. Additionally, a comprehensive analysis was conducted by comparing actual and simulated data in order to assess both the quantitative and qualitative aspects of the system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".