This paper identifies technology gaps on the path to achieve sustainable development of floating offshore wind in Japan. The key challenges emphasizing on unique conditions such as earthquakes and tropical cyclones are addressed. In Japan the absence of oil & gas has led to social challenges like lack of supply chain, infrastructure, and human resources in offshore wind. Site selection, design & operation, industry development, and contribution to national energy policy "S + 3E" are studied.

Laboratory testing campaigns for wind energy industry plays an essential role in testing innovative control strategies and digital twin applications. But incidents during testing can be detrimental and might cause project delays and damage to expensive equipment. We propose an anomaly detection scheme for laboratory experiments that are developed and tested to enhance reaction time and prediction quality, reducing the likelihood of damage to equipment due to human error or software malfunction.

A simulation model of a deployed offshore wind turbine was developed using real-world measurement data. The method shows how to obtain, update and validate a simulation model and allows to improve the efficiency and longevity of offshore wind turbines and support operation and maintenance decisions. Simulations were conducted to analyze the effects of turbulence and wind patterns on turbine lifespan, providing insights to improve maintenance planning and reduce operational costs.

We analyzed the reliability of wind turbine blade bearings under extreme turbulence wind conditions to improve their safety and performance. Using simulations, we studied how factors like turbulence and design variations affect failure probability. Our findings show that current standards may underestimate these risks and highlight the need for stricter controls on critical design parameters.

This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore methods of early fault detection. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large megawatt-scale offshore wind generators.