Wind energy capacity is set to grow tenfold by 2050 in order to mitigate climate change. Unfortunately, wind turbine (WT) operation and maintenance (O&M) cost is twice as high as photovoltaic and fossil fuel power plants due to high frequency of very expensive unplanned repairs. The major areas of issue are rotating components of turbine rotor and drivetrain including blades, hub, low-speed shaft, main rotor bearing, gearbox and generator. Repair costs are increased by high crane hire cost, or even more expensive jack-up vessel charter costs (~£90k/day) for offshore WTs. WTs are equipped with condition monitoring (CM) systems to detect drivetrain faults, however no system in the market or in development monitors blades (a glass / carbon polymer structure), and hub, low-speed shaft without embedded sensors or non-destructive testing / inspection techniques. Embedded sensors are difficult to retrofit and maintain. Inspection requires shutdown in order to carry out non-destructive testing or manual inspections. Optical and IR cameras, robotic and drone solutions exist but they are expensive, cumbersome or limited in types of faults that can be detected. Manual inspections present health and safety problems due to tower climbs and rope access work. The project will develop a radically novel automatic online CM / root cause analysis system capable of detecting blade / hub / shaft dangerous faults without embedded sensors. Technology is underpinned by sophisticated signal processing and AI to deliver a cost effective, easily retrofitted solution. Blade / shaft / hub fault detection unlocks access to a \>$4.23 billion market opportunity. Based on projected sales, the industry will save £893 million over five years by enabling inexpensive planned repairs rather than costly replacements and preventing fault re-occurrence by root cause detection / elimination. System sales will generate cumulative PBIT £10.5M (5 years), ROI 14.3 and PBIT 378M (10 years).

To develop a wind turbine generator monitoring solution for novel control and extended life operations of onshore turbines.

Natural Power is an independent renewable energy consultancy and service provider, headquartered in Scotland, with a vision to 'create a better environment'. We offer support and advice at all stages of a renewable energy project from the initial planning and development phase, through to providing assistance and expertise in operations and finally decommissioning and/or repowering of the assets. We are considered leaders in the field of operational services in onshore wind due to our holistic approach to renewable asset management, combining expertise from our analytics, asset management and servicing departments. To compete within a subsidy free marketplace both onshore and offshore wind farms in the UK need to reduce operational expenditure (OPEX) and hence reduce the overall levelised cost of energy (LCoE) of wind farm projects. In addition, to maximise returns on investments there is a drive to extend the life of operational assets and in order to do this the remaining useful life of the assets must be determined. Adopting a data driven approach is essential to ensure efficient decision making in these key areas. For the wind farms that Natural Power manage and service there is increased demand to provide leaner, smarter and more efficient services. To remain industry leading while being cost competitive it is essential that data are integrated into the decision-making process for the ongoing management of wind farms. An example of a success to date includes analysing data generated on common faults on turbines and combining our analysis and mechanical expertise to determine which faults can be automatically reset as opposed to being manually reset. This metric saves on average, seven hours of downtime per fault. Taking a data driven approach to wind farm operation will allow proactive maintenance to be undertaken at a time where the wind forecast is for low wind, reducing the overall financial impact of the turbine repair. This will also allow smart management of the site technicians to ensure they are targeting the areas which will have the biggest financial impact first. The main challenge is that the data are complex and disparate, as part of this project we are looking to combine data and analysis to produce actionable tasks. The knowledge gained from the data analytics not only benefits Natural Power but the entire industry since more electricity will available from renewable energy sources and this fits firmly with Natural Power's vision.