Health Monitoring of Offshore Wind Structures: Sensing Technology, Uncertainty, and Artificial Intelligence

Area of Science:

• Engineering
• Marine Technology
• Renewable Energy Systems

Background:

• Offshore wind farms are expanding into deeper, remote ocean regions, increasing the need for structural safety and operational reliability.
• Sensing technologies are crucial for monitoring structural and environmental conditions in harsh marine settings.

Purpose of the Study:

• To review sensing technologies for offshore wind turbines (fixed and floating, horizontal and vertical axis).
• To address data uncertainties in sensing and explore methods to improve accuracy.
• To evaluate AI and digital twin technologies for offshore wind power monitoring.

Main Methods:

• Comprehensive review of environmental monitoring, SCADA, condition monitoring, and structural health monitoring systems.
• Analysis of sensing uncertainty sources (marine environment, platform, operational variations, sparse sensing).
• Evaluation of AI and digital twin technologies for monitoring applications.

Main Results:

• Identified key sensing technologies and data transmission/placement strategies.
• Detailed analysis of sensing data uncertainty sources and their impact.
• Explored methods to overcome uncertainties and enhance sensing accuracy.
• Assessed the potential of AI and digital twin technologies.

Conclusions:

• Fusing multi-source data for intelligent decision-making and early warning systems is a key future direction.
• Addressing sensing uncertainties is vital for reliable offshore wind farm monitoring.
• This review supports the safe development of deep-sea offshore wind farms.