The rapid expansion of wind energy, particularly offshore and floating wind, has introduced new operation and maintenance challenges in ensuring the reliability, availability, and cost-effectiveness of wind turbines. Traditional operation and maintenance often struggle to address the increasing system complexity, harsh environmental conditions, and limited accessibility. In response, the integration of advanced technologies, such as Artificial Intelligence and Digital Twins, has emerged as a transformative approach for enabling real-time monitoring, intelligent diagnostics and prognostics, and informed maintenance decision-making. These technologies hold the potential to unlock new opportunities for future operation and maintenance of wind turbines.
Goal:
The aim of this research topic is to bring together cutting-edge research that advances our understanding of intelligent, digitalized operation and maintenance for wind turbines. By collecting interdisciplinary contributions from the international research community, the research topic will highlight recent advances in artificial intelligence, digital twin technologies, condition monitoring, fault detection, diagnosis, and prognosis, condition-based/predictive/prescriptive maintenance decision-making, etc. These solutions enhance reliability and availability, reduce lifecycle costs, and improve the resilience of wind energy systems, especially under challenging offshore, remote, or harsh environmental conditions. Through this collection, we seek to provide a comprehensive platform for researchers, engineers, and decision-makers, promoting interdisciplinary collaboration and accelerating the deployment of next-generation intelligent operation and maintenance solutions for wind energy worldwide.
Scope and information for Authors:
This Research Topic will cover advances in the operation and maintenance of wind turbines. We welcome original research, reviews, and case studies on, but not limited to:
• Operation and maintenance strategies for wind energy, including condition-based/predictive/prescriptive maintenance approaches • Artificial Intelligence techniques in enhancing operation and maintenance for wind turbines • Reliability modeling, risk assessment, and asset management of wind turbines and supporting systems • Advanced logistics planning and optimization of maintenance resources for wind farms • Applications of sensor data, digital twins, and data analytics for improving operational efficiency • Condition monitoring, fault detection, diagnosis, and prognosis for wind turbines • Techno-economic analysis and life cycle assessment of wind projects
Submissions that highlight interdisciplinary approaches, innovative digital solutions, or real-world demonstration projects are especially encouraged. Studies focusing solely on wind turbine systems without relevance to O&M, or work limited to materials development without operational context, will generally fall outside the scope of this Research Topic.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Perspective
Policy Brief
Review
Technology and Code
Keywords: Operation and Maintenance, Wind Turbines, Artificial Intelligence, Digital Twin, Condition Monitoring, Maintenance optimization
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
