The United Nations Sustainable Development Goals (SDGs) have called out the intent to substantially increase the share of renewable energy in the global energy mix by 2030. Currently, renewable energy sources are estimated to contribute to between 28-30% of global energy production. To meet the target of a sub-1.5 Celsius average global temperature rise, it is expected that more than 60% of global energy will be required to be produced by a renewable source by 2030, with wind power expected to play a substantial part in meeting these goals. Wind turbines are a complex piece of equipment comprising physical, mechanical, and electrical systems, with high, upfront capital investment, so insuring they can operate effectively, mostly in remote locations is paramount.
As the dependence on wind energy grows, so does the demand for larger wind turbines, because as the diameter of the wind turbines doubles the potential capacity quadruples, vastly increasing efficiency. The increase in size has created offshore and even floating deployments, including a project to build the world’s largest offshore windfarm, in the North Sea, which has begun powering British homes and businesses. Developers confirmed that Dogger Bank, which sits 70 nautical miles off the coast of Yorkshire, started producing power in October 2023, as the first of 277 turbines was connected to the electricity grid. The project will produce 3.6 gigawatts of power, enough for 6m homes, when it is completed in 2026.
Why digitalisation holds the key to upscaling wind turbine power generation
As the size of wind turbines increases, digitisation of the design and development cycle is vital so that wind turbine manufacturers can develop lightweight and more efficient wind turbine drivetrains and generators with higher power density. Two significant benefits enabled by digitalisation, include helping manufacturers achieve faster go-to-market and the ability to manage more cost-effective development and designs that would be practically impossible to achieve through traditional methods.
All processes associated with a wind turbine can be established as early as the prototype stage in a common data model: the digital twin allows you to digitally design and to test your wind energy plants before commencing series production.
This saves valuable time and costs, while at the same time increasing the engineering quality. The digital twin of a wind turbine also allows a simulation of the critical phase prior to commissioning – as well as safe implementation.
How utilising IoT and digitalisation ensures minimal wind turbine downtime
Once the wind turbines are up and running, digital representation through a digital twin is vital to optimise operations for high performance, reliability, and quality. Operating a wind turbine, with all the disparate electrical, mechanical and electronics components is a very complicated operation, whereby all the mechanisms need to operate collaboratively to ensure the safety, reliability and quality of the system.
Some wind turbine components are known to have higher failure rates, such as the generator, gearbox, blades, and bearings which are often caused by fatigue stresses, cracks due to strains on the blades, tower or gearbox components which can be caused by unfavourable weather conditions and even bird strikes. Having a digital representation allows for analysis and prediction of when these common failures will occur so they can be pre-emptively dealt with to ensure minimal downtime that can be costly and hazardous.
IoT, is emerging as a powerful enabler for renewable energy asset management and can also contribute to the industry achieving the UN’s Sustainable Development Goals (SDGs) by providing intelligence to optimise the design and manufacture of these machines. In partnership with IoT, digitalisation can be enabled to successfully create a digital twin, as data needs to be collected from all relevant parts of the product lifecycle via IoT-enabled sensors that deliver real-time data, which helps to predict how that system will behave in real-world conditions and improve accuracy.
Lifetime performance data gathered via IoT supported sensors can be extremely valuable for better utilisation and maintenance. For instance, sensors can record friction in the gearbox or oil contamination due to a malfunction, or acoustic devices can help record excessive noise and vibration that indicates failures. Additional sensors include vibration sensors for gear box monitoring and accelerometers for tower sway and blade monitoring. Manufacturers can obtain a wide range of operational data which is analysed through predictive analytics that uses machine learning. It tracks vibrations from the nacelle – the unit that houses the generating equipment to which the rotor and blades are attached – as well as oil levels and data from third-party sensors.
Digitising this information can help predict mechanical failure, while software tools can help predict what component will fail at what stage in how much time.
AI is pivotal to digital twins
Digital twins are coming of age for these applications – from computer-aided engineering (CAE) we have multi-physics simulation software and finite element analysis simulation, combined with machine learning (ML) software, and other data-driven artificial intelligence (AI) technologies are increasingly playing a crucial role in determining the choice of materials and manufacturing processes to ensure precision and quality standards. By simulating the design, engineering and production phases, wind turbine manufacturers can drastically reduce design cycles, and asset failures thereby saving millions of dollars.
During wind turbine validation and testing phases, AI/ML approaches can help focus efforts on the most critical tests and fill the gaps by accurately predicting results between data points derived from sensors and metrology. However, this requires high-quality data from CAE simulations in the virtual world and effective use of sensors and metrology data. This allows a reduction in the number of tests necessary, enabling robust wind turbine designs to be certified and deployed sooner. In operation, a digital twin of the physical asset can use data from IoT sensors to feed into the analysis to predict failures.
OEM’s can reap the benefits of getting to market faster and reducing cost by employing digital platforms and embracing the power of IoT. These tools can accelerate innovation and improve engineering design and productivity across the complete value chain right from component suppliers, turbine manufacturers, wind-farm operators/owners, and even independent power producers.
Xiaobing Hu is the Head of Applied Solutions at Hexagon’s Manufacturing Intelligence division.
