Infrastructures need active maintenance, as they are subject to environmental forces and general wear and tear. Inspections of structures that are difficult to access, such as wind turbines, are traditionally performed with high-rise equipment and specialized altitude technicians. These solutions are expensive, time consuming, and inefficient. Moreover, the quality of data obtained is low, leading to a high rate of missed diagnostics, which could have disastrous and expensive consequences.
Recently, drones have been adopted for wind turbine inspections as they allow safe and affordable access. Most of these drones are controlled remotely by people or are automated using GPS based waypoints. These methods are slow and inefficient, and depend heavily on weather and light conditions, leading to mediocre and unpredictable results. Furthermore, current drone solutions need several days to produce inspection results because they require cloud-based processing following data collection. The delay in obtaining results makes the inspection process time consuming and expensive, particularly because service providers often have to return to the field to repeat inspections due to low-quality data.
According to the European Wind Energy Association, wind turbine maintenance represents around 30% of the total levelized cost of electricity (LCOE) over the lifetime of the project. Wind turbine inspections represent between 35% and 50% of operations and maintenance (between 10% and 15% of the LCOE). According to the National Renewable Energy Laboratory (NREL, part of the U.S. Department of Energy), the cost of wind turbine inspections is €22,000 per MW for onshore structures and €67,000 per MW for offshore. At end of 2016, worldwide, total cumulative installed capacity from wind power amounted to 486,790 MW with a 15% annual growth, bringing the wind turbine inspection market size to €11,200 million annually, and projected to be €20,150 million by 2020. Therefore, there is a great opportunity for significant reduction of costs that will have a deep impact in the value chain.
This is important for society as it would allow to significantly reduce the cost of electricity generated by wind energy, allowing to make it more competitive when compared to conventional energy sources. This will lead to increase the adoption of wind energy or to provide cheaper electricity to society, preserving the environment and reducing the effect of global warming.
The main factors that affect the lack of automatization and airworthiness of RPAS for infrastructure inspection tasks (and especially for wind turbines) are the following:
- Relative navigation: some RPAS incorporate computer vision based navigation, they still navigate based on satellite navigation (GPS, Galileo, GLONASS). They cannot navigate relative to objects in the environment while tracking them. Hence, most systems are unsuitable for precision navigation closely around complex structures such as wind turbines.
- Autopilot performance: most commercial autopilots are designed to operate in good conditions where precise attitude estimate and control are not critical. This makes them unsuitable for operations in windy conditions or where high-precision navigation is required.
- Aerodynamics and stability: most airframes have rudimentary designs that aim to just put a camera in the sky with little regards towards aerodynamics and stability in high-winds. This makes their use limited, especially for infrastructure inspection tasks, such as wind turbines.
Intuitive and automatic: most manufacturers do not take into account that users do not have years of experience and might be working under strenuous circumstances. This hinders the usability of the platforms that can become a logistical problem.
The main goal of the WEGOOI project is to reduce wind turbine inspection costs by 90% with respect to traditional inspections and over 50% with respect to other drones. The main goals of the project are:
- Drastically reduce wind turbine inspection costs.
- Completely autonomous and automated inspections in 15 minutes, with real-time field results.
- Operate in winds of 15 m/s.
- Consolidate the operational procedure internationally, creating highly qualified jobs in Europe.
