Condition Monitoring OffshoreWind Turbine throughMachine Learning

The mix of energy supply worldwide has changed dramatically in the last few decades. Worldwide, in order to tackle climate change and increasing energy consumption, there has been a clear movement from fossils towards renewable and sustainable energy sources. Wind energy, for example, has generated 98% of Scottish electricity demand in October 2018, which has established a world-class record. Compared with onshore wind turbines, offshore wind could provide relatively larger capacity and a lower level of noise pollution, etc. As the wind industry moving to deeper water depth, fixed type wind turbines are no longer suitable, floating wind turbines must be applied. In addition, condition monitoring is being regarded as one of the finest solutions for the development of wind turbines in the O&M phase. In another word, much effort must be made in developing offshore wind turbine condition monitoring systems. A more cost-effective and more reliable offshore wind turbine condition monitoring technique is highly desired by the wind industry. Meanwhile, the fast-growing of data science, which will still be a promising field over the next 10 years, especially in the wind energy sector, will be applied in this project together with the development and analysis of the AHSE model for speed/power forecasting & predictions and fault detection & diagnosis.