Periodic Reporting for period 3 - WEGOOI (Wind Energy Generator Onshore and Offshore Inspector)
Période du rapport: 2019-11-01 au 2021-01-31
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.
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.
Navigation and analytics software update:
- Expanded the navigation trajectory from one to three blades in single flight.
- Expanded the trajectory state machine module to include new flight phases that cover all three blades from four sides each.
- Tested the new navigation module in Alerion's simulation environment.
- Successful outdoor tests in wind turbine
- Optimized the trajectory tracking module to sync computer vision and laser readings and obtain tracking accuracy of the laser readings
- Added autonomous precision landing on moving boat
Hardware platform update:
- Redesigned camera module in order to rotate, always keeping the camera perpendicular to the blade surface.
- Redesigned airframe to accommodate camera module.
- Flight tested the new airframe and camera module in 15m/s winds.
Onshore and Offshore field tests:
- Onshore demos in Spain
- Offshore demos in Denmark and Spain
Business analysis and dissemination:
- Key agreements with multinational clients.
- Redesigned website and Alerion brand to include WEGOOI project
- Created diffusion material: brochures, business cards, corporate video, roll-up
- Attended conferences and trade shows to present WEGOOI. 2018 AUVSI Exponential 2018 (Denver), AWEA WindPower (Chicago), 2019 AWEA WindPower (Houston), 2019 Wind Europe (Bilbao), 2019 Energy Drone Coalition (Houston), 2019 SPE Offshore (Aberdeen), 2019 Wind Europe Offshore (Copenhagen)
- Roundtable introducing WEGOOI in 2019 Wind Europe in Bilbao.
- Workshop and presentation on WEGOOI at Drone Energy Coalition.
- Conference booth at WindEurope Offshore 2019 in Copenhagen
- Organized demo for workshop in NREL in March 2020 (cancelled due to COVID-19 pandemic)
- Contacted relevant organisations for feedback and workshop organisation
- Organised meetings with key stakeholders in Spain, Portugal, France, Denmark, and United States.
- European patent granted approval
- Patent extended to 20 countries.
- Registered trademark in EU and US
- Market research study
- ISO9001:2015 continuation and implementation
- Expanded the navigation trajectory from one to three blades in single flight.
- Expanded the trajectory state machine module to include new flight phases that cover all three blades from four sides each.
- Tested the new navigation module in Alerion's simulation environment.
- Successful outdoor tests in wind turbine
- Optimized the trajectory tracking module to sync computer vision and laser readings and obtain tracking accuracy of the laser readings
- Added autonomous precision landing on moving boat
Hardware platform update:
- Redesigned camera module in order to rotate, always keeping the camera perpendicular to the blade surface.
- Redesigned airframe to accommodate camera module.
- Flight tested the new airframe and camera module in 15m/s winds.
Onshore and Offshore field tests:
- Onshore demos in Spain
- Offshore demos in Denmark and Spain
Business analysis and dissemination:
- Key agreements with multinational clients.
- Redesigned website and Alerion brand to include WEGOOI project
- Created diffusion material: brochures, business cards, corporate video, roll-up
- Attended conferences and trade shows to present WEGOOI. 2018 AUVSI Exponential 2018 (Denver), AWEA WindPower (Chicago), 2019 AWEA WindPower (Houston), 2019 Wind Europe (Bilbao), 2019 Energy Drone Coalition (Houston), 2019 SPE Offshore (Aberdeen), 2019 Wind Europe Offshore (Copenhagen)
- Roundtable introducing WEGOOI in 2019 Wind Europe in Bilbao.
- Workshop and presentation on WEGOOI at Drone Energy Coalition.
- Conference booth at WindEurope Offshore 2019 in Copenhagen
- Organized demo for workshop in NREL in March 2020 (cancelled due to COVID-19 pandemic)
- Contacted relevant organisations for feedback and workshop organisation
- Organised meetings with key stakeholders in Spain, Portugal, France, Denmark, and United States.
- European patent granted approval
- Patent extended to 20 countries.
- Registered trademark in EU and US
- Market research study
- ISO9001:2015 continuation and implementation
The progress beyond the state of the art is the following:
- Alerion's drones are able to fly 3 meters from the structure without the need to use GNSS, keeping a constant and safe distance to the structure, while maintaining it always centered. Other RPAS must either be flown manually at a safe distance, usually around 10-20 meters.
- Alerion's drones know exactly which blade (A, B, C) and face (leading edge, trailing edge, pressure side, suction side) they are looking at, and the distance from the root and tip. Other RPAS platforms fly using GNSS based waypoints without knowing the information about the structure.
- Images are analysed in real-time vs 72 hours of processing needed by other RPAS.
- Full inspection report is provided upon landing, vs 1 week needed by other RPAS.
Our intelligent and efficient inspector drone provides for high-quality real-time wind turbine inspections, helping customers to drastically reduce operation and maintenance costs by up to 80% with respect to traditional methods, and at least 50% with respect to other RPAS based methods. This would allow to reduce the cost of electricity by at least 10%.
- Alerion's drones are able to fly 3 meters from the structure without the need to use GNSS, keeping a constant and safe distance to the structure, while maintaining it always centered. Other RPAS must either be flown manually at a safe distance, usually around 10-20 meters.
- Alerion's drones know exactly which blade (A, B, C) and face (leading edge, trailing edge, pressure side, suction side) they are looking at, and the distance from the root and tip. Other RPAS platforms fly using GNSS based waypoints without knowing the information about the structure.
- Images are analysed in real-time vs 72 hours of processing needed by other RPAS.
- Full inspection report is provided upon landing, vs 1 week needed by other RPAS.
Our intelligent and efficient inspector drone provides for high-quality real-time wind turbine inspections, helping customers to drastically reduce operation and maintenance costs by up to 80% with respect to traditional methods, and at least 50% with respect to other RPAS based methods. This would allow to reduce the cost of electricity by at least 10%.