The Blog - Wind energy market analysis

Posted 11/12/2018

Richard Heap

   

How do digital twins make wind turbines smarter?

Digital twins can use big data to help wind farm owners and operators to boost the performance of their turbines. Richard Heap spoke to DNV GL's Graeme McCann.

Wind turbines in the office rawpixel.com via Pexels

We’ve all heard of ‘big data’. We might not understand exactly what it means, or how we can use it to improve our lives, but it’s got ‘big’ in the name so must be good. 

That said, the main principle of ‘big data’ is quite simple. The idea is that we can collect information from large data sets and then use it to identify patterns and trends, in areas including human behaviour. Businesses can then use it to sell more products and services.
 
That’s the theory. Doing it is rather more complex, hence the old joke comparing big data to teenage sex: everyone talks about it and hears that all their peers are doing it but, in reality, no-one really knows how to do it properly if they’re even doing it at all.
 
One way that businesses in the wind sector are increasingly looking to make use of big data is through the use of ‘digital twins’. This is where wind farm owners use data from a range of sources – turbine specifications, wind speeds, site layouts – to build a digital replica of the project.

In an interview with A Word About Wind, DNV GL’s head of turbine engineering Graeme McCann called it a “virtual representation of a physical asset”.
 
By doing this, McCann said wind farm owners can gather useful insights on how to optimise the financial performance of their projects.

This could include using weather forecasts to determine how well a scheme is likely to perform on a given day and manage it accordingly; and how hard an owner should work each turbine to maximise its performance.

Do digital twins boost returns?

Gathering information on how hard turbines have been working can also help owners to develop proactive strategies to manage anticipated maintenance problems before they happen, and test out these strategies using the information gathered.

In addition, owners can benefit if they can make better decisions on the risks and rewards of using turbines beyond their operational life, for example, or whether it makes sense to repower projects. These both represent major investments for wind farm owners. In the US, the question of repowering versus lifetime extension has also attracted attention because of the imminent expiry of the wind energy production tax credit.

To find out more about repowering in the US, check out this piece by Ilaria Valtimora.
 
McCann said that producing ‘digital twins’ of wind farms could play an important role as project owners plan their strategies.

“Those models then become living things,” he said. “They don’t stay static. They learn from the operation of the asset, and update themselves based on the information of how the asset is really performing.”
 
This would then enable owners to make a series of marginal improvements in their wind farms. 

For example, McCann said predictive maintenance strategies could help to improve the availability of turbines by an undisclosed number of percentage points; and advanced wind farm control systems could similarly add 1%-1.5% to energy yield. If an owner could make improvements like these on a 50MW project, the effective gains could enable them to add an extra turbine to the site. For more on optimising wind farm performance, click here
 
“There’s no quantum leap here, but this can facilitate a large number of incremental gains,” he said. “When you add them up you can have a big effect.”

Digital technology in turbines

The idea of ‘digital twinning’ is not a new one: the aerospace sector has used mathematical modelling to similar effect since the 1970s to help it optimise its planes. Likewise, wind turbine makers have used comparable technology to improve their turbines’ performance.
 
But using it to improve the performance of whole wind farms, and pulling in data from a wide range of sources, is fairly new.

General Electric came out with its ‘digital wind farm’ concept in 2015, to use both hardware and software to optimise the performance of its projects, and has been investing heavily in improving the performance of its turbines since then. Its €1.5bn acquisition of blade tech specialist LM Wind Power, which concluded in early 2017, is another example – though not strictly related to its digital investment strategy.

GE is not alone of course. All of the large turbine makers are looking at ways to use digital technology to improve the performance of their turbines. The buyout of Utopus Insights by Vestas for $100m in February 2018 is another example.

And DNV GL launched a platform called Veracity in February 2017 to help firms make use of big data. This brings together information from a range of sources to model projects’ performance. As of May 2018, it had more than 1million service subscriptions from 1,500 different companies, and it also that month teamed up with maritime data firm Arundo.
 
McCann said that a good digital twin would allow lots of disparate simulation models to be brought together from a range of stakeholders from across the industry.

This would then raise a further challenge: companies need to decide not only what data they need to come up with an accurate model, but also make sure they are comfortable sharing performance data that could be commercially sensitive.
 
These relationships will need to evolve if wind companies are to make the most of big data and digital twinning. In that crucial respect, big data isn’t exactly like teenage sex. It’s also about the long-term relationships that come with it.

This piece was originally published on 31/03/17 and last updated on 11/12/18.

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