Recent Advances in Wind, Wave, and Tidal Energy Systems: Modeling, Control, and Intelligent Technologies
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".
Deadline for manuscript submissions: 25 November 2026 | Viewed by 35
Special Issue Editor
Interests: vibration analysis; power systems; electromechanical integration; structural health diagnosis and systems; smart grids; wind power; photovoltaics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the rapid development of offshore wind power and marine renewable energy technologies has played an increasingly important role in the global transition toward sustainable and low-carbon energy systems. As the installed capacity of renewable energy systems continues to grow, issues related to operational reliability, energy conversion efficiency, system stability and grid integration have become critical challenges for both researchers and engineers.
At the same time, modern renewable energy systems are becoming more complex due to the integration of advanced sensing technologies, power electronics, intelligent monitoring systems and large-scale grid-connected operations. To improve system performance and long-term operational stability, researchers have devoted significant attention to areas such as fault diagnosis, predictive maintenance, intelligent control strategies, energy management and data-driven modeling methods.
Recent progress in artificial intelligence, machine learning, digital twin technologies and smart grid applications has provided new opportunities for improving the operation and maintenance of renewable energy infrastructures. These emerging approaches not only enhance system reliability and operational efficiency but also support more effective lifecycle management and resilient energy system integration.
This Special Issue aims to provide a platform for researchers, engineers and industry experts to present recent advances in wind and marine renewable energy systems, particularly in the areas of intelligent technologies, system reliability, advanced control methods and grid integration applications. Both theoretical studies and practical engineering applications are welcome.
Potential topics include, but are not limited to, the following:
- Intelligent control of wind, wave and tidal energy systems
- Renewable energy system reliability and resilience
- Artificial intelligence and machine-learning applications
- Condition monitoring and predictive maintenance
- Fault diagnosis techniques for renewable energy systems
- Smart grids, microgrids and hybrid energy systems
- Power electronics and energy conversion technologies
- Energy storage coordination and power quality improvement
- Offshore wind farm operation and optimization
- Digital twin and data-driven modeling technologies
- Renewable energy forecasting and energy management
- Grid integration and system stability analysis
We hope that this Special Issue will promote interdisciplinary collaboration and encourage the exchange of innovative ideas and practical experiences related to next-generation renewable energy technologies.
Prof. Dr. Ming-Hung Hsu
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
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Keywords
- intelligent control
- wind and marine renewable energy
- offshore wind energy
- wave and tidal energy
- renewable energy reliability
- grid integration and smart grids
- microgrids
- power electronics
- energy storage systems
- fault diagnosis and predictive maintenance
- condition monitoring
- digital twins
- artificial intelligence and machine learning
- data-driven modeling
- energy management and optimization
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