Introduction
Offshore wind energy plays a crucial role in the global transition toward sustainable and low-carbon energy systems.
However, the operation and maintenance (O&M) of offshore wind farms remain challenging due to harsh environmental conditions, complex asset interactions, and the high costs associated with maintenance activities. Traditional risk assessment approaches are often reactive, relying on historical failure records and expert judgement, which limits their ability to support timely and proactive decision-making.
Research Aims
This project aims to develop an AI-assisted framework for risk assessment and operational decision support in offshore wind farms, enabling more reliable, efficient, and resilient wind energy production.
The research will focus on integrating diverse sources of operational data, including SCADA measurements, condition monitoring data, maintenance records, weather information, and inspection reports. Advanced artificial intelligence techniques such as machine learning, graph neural networks, and large language models will be employed to automatically identify emerging risks, detect abnormal operating conditions, and uncover hidden relationships among technical, environmental, and operational factors. By combining data-driven analytics with probabilistic risk modelling, the project will establish a dynamic risk assessment framework capable of continuously evaluating the health and vulnerability of offshore wind assets under uncertain operating conditions.
A key component of the research is the development of an intelligent decision support system that translates risk insights into actionable maintenance and operational recommendations. The proposed system will assist operators in prioritising maintenance activities, allocating resources, and responding to adverse weather events while balancing safety, reliability, and economic performance. Explainable AI techniques will be incorporated to improve transparency and trust in the generated recommendations, ensuring that human operators remain central to the decision-making process.
The project will also investigate the resilience of offshore wind farms against extreme weather events, cascading failures, and long-term climate-related uncertainties. Through scenario analysis and digital twin technologies, the research will evaluate how offshore wind systems can adapt to disruptions and recover from failures more effectively. The expected outcomes include novel AI-driven methodologies for risk management, enhanced operational reliability, reduced maintenance costs, and improved resilience of offshore renewable energy infrastructure. The developed framework will contribute to the advancement of intelligent asset management strategies and support the sustainable expansion of offshore wind energy worldwide.