But how will exploitation of these technologies impact on the cost of energy generated by wind turbines and help to accelerate the transition to a low-carbon future?
To answer that question, we need to consider how the industry has developed over the past few decades and what further steps it should take to continue to drive wind technology forward.
"From farm machine to floating power station" is one way of summarising the development of wind turbines over the past 30 years.
This remarkable transformation from relatively small and simple machines constructed using off-the-shelf mechanical components and blades manufactured applying boat-building technology, to the huge aerospace-grade devices we now see being erected off our coastlines has, to a significant extent, been made possible by the parallel development of increasingly sophisticated design standards, tools and methods.
To date, the approach towards design has been of a deterministic nature. The loading and resistance properties of structures are defined using unique, characteristic levels.
These levels are subsequently modified using load, material and consequence safety factors with the remaining margin between load and resistance representing the residual safety level for the given limit state.
It is probable that these standards, when followed correctly and within their bounds of application, generally result in conservative designs, which translates into unnecessary cost.
Given the potential to reduce costs by adopting a different approach, those at the forefront of turbine design should aim to move beyond deterministic methods and take full advantage of the digital tools now at their disposal. Two interrelated approaches to design and analysis should be pursued.
Probabilistic analysis methods, initially developed for the oil and gas and aerospace industries, allow for a greater level of optimisation by quantifying sources of uncertainty and then tracking how the uncertainties propagate through the physical model used in the design.
The design’s robustness can also be assured through sensitivity analyses.
This approach requires higher levels of analysis and relies on the collection, management and processing of large amounts of measured data to help quantify all the modelling uncertainties. But the barriers to its adoption are being broken down by advances in big data and cloud computing technologies.
Multidisciplinary design, analysis and optimisation (MDAO) incorporates probabilistic analysis methods and can make full use of these ongoing advancements in computational resource and data handling.
This method disrupts the traditional approach to engineering design where teams, each with a specific area of expertise, would sequentially focus on one aspect of the design.
For example, aerodynamicists would define the shape of a turbine blade, within which?the structural engineers would be expected to fit their design.
MDAO balances the importance of a range of factors, including performance, weight, manufacturability, reliability, and maintainability, at all stages of the design process.
By incorporating all relevant disciplines simultaneously rather than sequentially, the interactions between disciplines can be exploited.
However, this approach does significantly increase the complexity of the design process, requiring the use of more data and more analysis to be performed.
MDAO is used in a wide range of fields, but has been applied most extensively within aerospace engineering, where lifecycle cost replaced performance as the primary objective of design in the 1970s and 80s.
The principles behind MDAO can also be applied in the operational phase of a wind turbine to take full advantage of the explosion in the availability of sensor data that is being enabled by advances in cloud computing and big data.
By collecting, managing and analysing real-time data from the condition monitoring systems (CMS) and supervisory control and data acquisition systems (Scada) connected to all the turbines on a wind farm, it will be possible to continuously optimise the operational performance and manage the life of the assets.
The widespread adoption of MDAO by the wind industry would take full advantage of the opportunities offered by digital solutions, such as big data and cloud computing to further accelerate the transition to the low-carbon future that we all wish to see.
David Witcher is turbine engineering business manager at DNV GL