Improved operations, a reduction in unplanned outages and better managed variations in market conditions à¢€” it’s the idyllic aim of power leaders across the globe, but how is the digital twin concept making this goal a reality?
Digital twinning describes the mapping of physical assets to a digital platform.
For the energy industry, this could be a windfarm, nuclear facility or traditional coal plant. The digital replica uses data from physical assets, for instance, the data acquired from the motor of a wind turbine, to analyse its efficiency, condition and real-time status.
It sounds impressive, but how can it improve energy applications?
Consider windfarms as an example. Windpower contributed 15 per cent of Britain’s total electricity generation in 2017.
It’s an increasingly popular form of energy generation, but as turbines are expected to operate over many decades, in some of the harshest environments, unexpected failures can be costly. Therefore, it’s highly beneficial to predict an upcoming equipment failure, well before it takes place.
Using a digital twin of the turbine, the replica can inform an operator when an asset begins to show signs of non-optimal performance, without an engineer having to access the physical turbine. This could give an indication of when the asset could fail, minimising the risk of unexpected downtime.
By gaining this insight into upcoming issues, maintenance engineers can also make their repair decisions based actual data, as opposed to pre-defined maintenance schedules or guesswork.
Assets can then be kept at their optimal level for maximal profits, rather than performing random, reactive maintenance when a part breaks down.
That said, improved maintenance isn’t the only advantage of this technology. Unlike nuclear, gas or coal, where the power output can be predicted, windpower is much more volatile. Using a digital twin of a windfarm, operators can forecast when they may experience increases or decreases in wind speed based on simulations.
By analysing digital turbines, operators can utilise this data to optimise generation and profits. What happens if the wind drops below a certain level? Should you sell your energy today based on rate trends? Would it be better to cease generation today, because the cost of generation surpasses potential profit? All of these questions could be informed and answered using this data.
However, data collection holds no value without the correct software. An intelligent software package is essential to analyse and visualise this data onto a human machine interface (HMI). COPA-DATA’s monitoring software zenon, for example, includes a simulation mode which allows operators to make changes to a digital asset and see the effects à¢€” without the risk of tampering with a real-life physical asset. à‚
Digital twinning can be applied to almost any energy generation site. At nuclear facilities, this technology could be used to predict the failure of a compressor, 30 to 60 days ahead of actual break down. It can cost millions to bring a nuclear plant back online, so the cost savings for this sector are undeniable.
Coal plant operators can also benefit from digital twin insight, to save on fuel and improve overall efficiency. This could reduce the amount of coal needed to run a plant, reflecting the huge potential for economic and environmental benefits.
Improved operations, reduced outages and better managed variations in market conditions may seem idyllic, but the digital twin makes it possible. It certainly explains why this technology is so well received in the energy sector, and why we’re set to see more use of digital twins in the future.
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