How might big data change the wind industry?

We all know that big data has the potential to transform many different types of industry: but what are the particular challenges of applying it to wind? Frances Salter investigates

We’ve all heard about how big data will reshape industries across the world – but what you might not know is that the wind industry is one of the most promising for big-data application.

The premise of this is simple: through using big data, companies can foresee and fix problems more easily, as well as streamlining regular maintenance. For these reasons, it’s often a key topic at wind industry conferences.

But what is less clear is exactly how this will look in practise.

How might the industry use big data?

One way 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.

Digital twins are used by many kinds of company already, of which General Electric and Siemens are some well-known examples. It’s a development which has been made possible by cheaper and better sensing, processing and data transmission. But, whilst the concept has been in use for several decades, using it to improve the performance of whole wind farms and pull in data from a wide range of sources is still relatively new.

Though the technology being used is still comparatively young, there’s clearly a huge potential market in wind power.

It could allow wind farm owners to gather useful insights on how to optimise the performance of their projects, as Graeme McCann of DNV-GL explained in an interview with us last year.

These insights could include using weather forecasts to determine how well a scheme will perform on a given day, and how hard each turbine should work to maximise performance. He even predicts that advanced wind farm control systems could add up to 1.5% to energy yield.

The benefits of data analytics

According to Romax Insight, collecting data is just one piece of the puzzle: in order to see the full impact of data analytics, the whole operations and maintenance cycle must be integrated into a digital decision-making platform. This is because of the fact that, whilst existing tools can alert a site team to impending turbine failure, they don’t yet have the inbuilt operational logic to assist with cost-effective solutions.

Using big data effectively will also require companies to work together: DNV-GL, for example, launched a platform in 2017 called Veracity, which brings together data from varied sources to model projects’ performance. This requires companies to decide how much performance data they are comfortable sharing, as it could be commercially sensitive.

These are both issues that companies will need to think through in deciding how best to make use of the potential for big data analytics. If used well, it could speed the process of wind becoming an increasingly mainstream and affordable energy resource.

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