Operational Availability Assessment of Tidal Stream Turbines Using Environmental Data and Fuzzy Logic Inference
TL;DR
A novel operational condition monitor that has a data-driven predictive mechanism for determining the instant states of each tidal stream turbine is proposed, reducing in uncertainty and the association with real-time operating conditions, which enable optimal scheduling decisions.
Abstract
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.