The Technical University of Denmark (DTU) has inaugurated a laboratory for the development of a modular robot for use in offshore wind turbine platforms, the oil and gas industry and fish farming.
The robot will be used for inspection initially, with the long-term vision that it will be able to carry out underwater repairs on foundations and rigs. Currently, a remotely operated vehicle (ROV) with relevant personnel must be hired to perform such tasks. These operations are both costly and weather-dependent.
“We have a vision of creating a modular robot that is simple to operate, without the need for technical experts,” says Associate Professor Roberto Galeazzi, DTU Electrical Engineering. “Initially, it will be able to inspect and monitor, for example, the condition of the foundation of a wind turbine under water, or an ocean fish farming plant. In the long term, the intention is for the modular robot, which comprises several independent robots (modules) that can both work coupled together and individually, to also carry out repairs on the foundation.
“Others are working on similar projects, but our robot differs in one key area. It is modular - which means that each individual part of the robot is able to work alone - or together with other modules. Working in unison, the robots can draw on each other’s functions, thereby becoming fully autonomous. For example, if a robot has problems with his propeller, it can connect with a second module and move using its propeller. The same applies to many other areas—for example in relation to having sufficient battery performance,” says Galeazzi.
It is envisaged that the underwater robot will be permanently installed on an underwater foundation where it can monitor and operate independently of the weather conditions. For example, using just a single module, the modular robot could install and replace sensors in a subsea docking station placed on the foundations of a wind turbine to provide continuous monitoring.
“The trend is moving towards developing small autonomous underwater vehicles (AUVs) to replace the current large and heavy ROVs. This requires the equipment used for underwater inspection and repair to be compressed, but the development is already undergoing. The concurrent development of an autonomous underwater robot therefore holds out enormous potential, both for the oil and gas industry and offshore wind farms,” says Business Area Manager Ole Nørrekaer Mortensen, FORCE Technology. FORCE Technology has more than 20 years’ experience of inspecting subsea oil rigs and pipelines, in particular.
Above the Waterline
Researchers from DTU have also developed a simple method for monitoring wind turbines using the symmetry created by the wind turbine when the blades rotate.
Previously, wind turbine monitoring and fault diagnosis have used detailed models or data-driven methods. This has placed significant demands on the model or data, which had to reflect the normal situation extremely accurately in order to detect irregularities, just as it can be very difficult to use these methods for identifying the location of a possible fault on the wind turbine.
A new method developed by Associate Professor Henrik Niemann’s research team from DTU Electrical Engineering, as well as DTU Compute and DTU Wind Energy makes it possible to both monitor the wind turbine while simultaneously establishing the location of any wear or fault, as well as the nature of the fault.
“We use data from sensors positioned at the point where the wind turbine blade is attached to the axis. There are two torque sensors on each blade. These are sensors which are already found on newer wind turbines in order to monitor the blade load. A wind turbine has three blades, so this provides three sets of measurements in total. We collect the data and then conduct a simple signal analysis. If the turbine is functioning as it should, the picture from the measurements will be completely symmetrical, whereas there will be fluctuations if there is a fault on one of the three blades,” says Niemann.
The measurements not only provide information on whether there is a fault with the wind turbine, but also the location and nature of the fault.