Enhanced Monitoring for Offshore Wind Turbines using Digital Twin and Artificial Intelligence

Abstract

Japan’s extreme environmental conditions have some potential challenges in the offshore wind development, and it requires enhancing the monitoring and maintenance practices. This study proposed an offshore wind turbine remote monitoring and advanced visual inspection by integrating digital twin technology, virtual reality, drones, and artificial intelligence (AI). An overview of Ramboll’s digital enabled asset management (DEAM) approach was elaborated, and its current advantages such as possible structure’s lifetime extension, failure mechanism detection and prevention, understanding the structure’s actual dynamic properties, environment monitoring, etc. In addition to that, two deep learning models that aimed to identify the OWT component and segment damages were trained, this might help to reduce the manpower, equipment cost, and lessen the visual inspection time. Furthermore, the challenges and conceptual possible integration of AI to drones during visual inspection were elaborated. The proposed method will include risk assessment and drone flight planning optimization in future studies.