Data: Unlocking the potential of existing wind assets
Wind energy is no longer new. With the first flush of youth (turbocharged by subsidies) behind some turbines but many years ahead before mandatory retirement, these early-mover assets can pose challenges for owners and operators.
The chances are that turbines installed in the early 2000s will have been technologically surpassed by bigger, more modern turbines capable of providing a lower levelised cost of electricity (LCOE). This leaves a key question for owners - should they persevere for as long as possible, or dismantle and upgrade to the newest models?
The latter option, known as ‘repowering’, is certainly popular. However, tearing down and replacing perfectly serviceable turbines can also be seen as a rather drastic option:
It’s expensive
Has a carbon cost for manufacture, transport and installation
As well as recycling challenges for the old ones
There are lower-investment, lower-risk options to explore first. And data should be the first place to look.
Building up inefficiencies
A new turbine or wind farm can generally be assumed to run pretty efficiently. But as time goes by, inefficiencies creep in. One common issue is blade surface degradation – a slow-build issue with a gradual impact on turbine efficiency. Other failures can be more abrupt but become more likely as time goes by. We have noticed one common drivetrain hasn’t met its suggested shelf-life, but this has only become clear as we’ve built up more data.
Turbines have also changed hands meaning the company running the asset today is often not the one which designed and commissioned it. In theory, this should cause no issues – we do know knowledge transfer isn’t always perfect.
Turning data into value
Optimising wind asset performance must surely play a role in the future of asset life extension, but how?
First, look at the data you have – there’s plenty of untapped knowledge and value there already.
For example, older turbines often exhibit yaw misalignment, reducing the asset’s efficiency. Existing data can usually identify that quickly.
This data assessment can be used in multi-turbine approaches too – looking for those with particularly high or low load metrics. Operators can then make intelligence-led decisions on which assets can be sweated for more value by running at increased load and which are vulnerable to excessive fatigue. This has the added benefited of sweating all assets to reach end of life at the same point.
Maximise current insights
It’s a myth that using this data requires a significant investment on time and resources.
While this may be true if the goal is to eke every theoretically possible drop of value from an asset, in our experience smart turbine engineers supported with built-for-purpose data analytics tools can achieve a great deal with the data already held within the organisation.
The most valuable data is what you already have, the key to unlocking its potential is to analyse it. This doesn’t require complex analyses which are expensive, time consuming and don’t scale. Begin with assessing the existing, underutilised data.