Maintenance of Wind, Wave and Tidal Energy Systems

Topic Information

Dear Colleagues,

The popularity of renewable energy sources has increased considerably in the past decade. In many countries, wind, wave and tidal energies are identified as the most promising energy sources. However, the maintenance of wind, wave and tidal energy systems is still a developing area.

For this Topic, we invite submissions that address novel research and the development of maintenance technologies for wind, wave and tidal energy systems, including, but not limited to, the following:

• Condition monitoring for wind, wave and tidal energy assets for stationary and non-stationary operations;
• Prognosis for wind, wave and tidal energy assets;
• Signal and image processing for wind, wave and tidal energy assets;
• Root cause analysis for wind, wave and tidal energy assets;
• Modelling and finite element analysis for fault diagnosis, prognosis and root cause analysis for wind, wave and tidal energy assets;
• Model-driven and data-driven approaches for wind, wave and tidal energy assessments.

Prof. Dr. Len Gelman
Prof. Dr. Shuncong Zhong
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Clean Technologies cleantechnol	5.9	9.4	2019	20.9 Days	CHF 1800	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Journal of Marine Science and Engineering jmse	3.2	5.6	2013	15 Days	CHF 2600	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

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Published Papers (1 paper)

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All Clean Technol. Electronics Energies JMSE Sustainability

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25 pages, 5676 KB  

Open AccessArticle

Operational Availability Assessment of Tidal Stream Turbines Using Environmental Data and Fuzzy Logic Inference

by Ali Fituri, Abdelouahed Gherbi and Hmeda Musbah

J. Mar. Sci. Eng. 2026, 14(16), 1471; https://doi.org/10.3390/jmse14161471 - 10 Aug 2026

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, [...] Read more.

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. Full article

(This article belongs to the Topic Maintenance of Wind, Wave and Tidal Energy Systems)

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