Fituri, Ali, Gherbi, Abdelouahed et Musbah, Hmeda.
2026.
« Operational availability assessment of tidal stream turbines using environmental data and fuzzy logic inference ».
Journal of Marine Science and Engineering, vol. 14, nº 16.
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Résumé
In hybrid energy systems, maintaining an optimal scheduling strategy for real-time distribution systems, particularly in triple hybrid power generation units, remains a critical challenge. The lack of an efficient real-time observability platform for off-grid hybrid units directly impacts scheduling priorities. In this work, a novel operational condition monitor that has a data-driven predictive mechanism for determining the instant states of each tidal stream turbine is proposed. Environmental variables are first preprocessed using a multivariate fuzzy logic system to generate informative features, which in turn are used by a machine learning classifier to identify the turbine availability states. The classifier is evaluated using K-fold cross-validation and robustness under increasing environmental noise levels. The main contributions of this work are the reduction in uncertainty and the association with real-time operating conditions, which enable optimal scheduling decisions. The baseline XGBoost classifier achieved an F1-score that increased after adding fuzzy-derived features. Comparative evaluation under noise-free and increasing noise levels demonstrates that the proposed framework consistently outperformed the baseline model while maintaining robust classification performance.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Chercheur(-euse): | Chercheur(-euse) Gherbi, Abdelouahed |
| Affiliation: | Génie logiciel et des technologies de l'information |
| Date de dépôt: | 25 sept. 2026 20:42 |
| Dernière modification: | 27 sept. 2026 18:21 |
| URI: | https://espace2.etsmtl.ca/id/eprint/34431 |
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