The offshore wind industry is experiencing significant growth in the UK at the moment, something which was boosted even further recently following the announcement of the industry Sector Deal by the UK government
The Deal will deliver £48 billion of investment in the UK’s offshore wind infrastructure, will create 27,000 jobs and will ensure that by 2030, 30% of all electricity in the UK is generated from offshore wind sources.
Meanwhile, the emergence of big data and digitalisation as a transformer of the global economy has been one of the key industrial trends of recent years. Last year, the Government’s Industrial Strategy identified artificial intelligence and data as one of its four Grand Challenges, providing clear evidence of the technology’s emerging importance, and highlighting its transformational potential.
The technology provides endless opportunities for innovation in the offshore wind and other renewable energy sectors. From reducing turbine downtime through predictive maintenance, to improving health and safety through better weather and wave height forecasting, the industry is still scratching the surface of the potential benefits.
The data opportunity
Taking offshore wind power as an example, it is now a mainstream source of electricity generation that is cost-competitive with conventional methods. The UK also has a world-leading marine energy supply chain that has the potential to achieve significant cost reduction.
Despite the significant progress being made and current levels of growth, there are still many challenges to be overcome in the design, operations and maintenance (O&M) of offshore wind farms. O&M represents around 25–30% of a wind farm’s lifetime cost but if offshore wind farms are to remain profitable, these costs need to come down. As a result, the industry’s focus has shifted towards smarter maintenance and task planning, and more efficient spare parts management and logistics, in an effort to reduce expenditure. One of the biggest challenges in achieving this will be finding out where and how improvements can be made, but this is an area in which data and digitalisation will play an important role.
Offshore wind turbine generators are complex systems comprising thousands of individual components and sensors. As each turbine is generating clean electricity, their numerous sensors are logging information and statistics about the condition and performance of everything from the equipment to the technicians who work on them.
The opportunities that all of this data presents for windfarm owner/operators are endless. Proper data management and analysis can help aid decision-making to improve efficiency, reduce running costs and solve operations and maintenance issues, thus resulting in long term benefits such as increased asset life span. In a sense, the data can ‘de-risk’ an investment; decision-making informed by data is always more dependable and reliable than decisions made on instinct, anecdotal evidence, or estimation.
For example, turbine inspection schedules can be planned because data indicates that they might need attention. The knowledge can then be used to equip engineers with the tools needed for specific tasks, creating efficiencies in logistics. It can also help to improve employee safety by reducing the number of visits to turbines and therefore the number of technician transfers from crew transport vessel to turbine. This is something which is an ever-present challenge for the industry.
Overcoming the challenges
Taking advantage of the opportunities offered by data and digitalisation can require significant changes for businesses, and the adoption of this new technology is one of the biggest challenges currently facing the renewables sector.
Many organisations in the offshore renewables sector need to overcome significant barriers such as behavioural change and replacing outdated IT systems. The most basic steps in the digital transformation often require new ways of working: reviewing data, active condition monitoring and taking steps towards preventative maintenance, for example. Besides simply using data and actioning the insights that it can provide, making a business digital-ready also involves a transition to the use of digital hardware and software tools to improve operational processes. These can include the provision of Wi-Fi connectivity on wind farm sites, or the automation of procurement processes to improve purchasing efficiency.
The transition to data-driven decision-making calls for a step-based approach. Looking at other industries where digitalisation is more mature, such as IT, finance, oil and gas, we can see four main stages:
- Descriptive: finding out what happened.
- Diagnostic: finding out why it happened.
- Predictive: working out what is likely to happen in future.
- Prescriptive: working out what needs to be done.
Transitioning to a data-driven process is not only about putting these steps into place, but also about implementing a system where the full value of the data is extracted at each step. While most individuals and organisations will work with Key Performance Indicators (KPIs), which provide valuable descriptive insight, different business functions require specific KPIs for them to be relevant and useful.
Taking advantage of the opportunities offered by data and digitalisation will require significant changes for businesses. However, it is now clear that the application of data analysis can cut costs and help owner/operators make smarter decisions. Becoming a data-driven business means climbing up the digital maturity model. The best way up is by leveraging analytics capabilities from the descriptive and diagnostic levels, and ultimately ending up at the predictive and prescriptive stages, from where progress can be made.
About the Author
Peter van Heck is Data Scientist, Offshore Renewable Energy (ORE) at Catapult. Peter is part of the Data & Digitalisation team at ORE Catapult, a multi-disciplinary team of experts in computer science, data management, mechanical engineering and marine technology. The team was established to address the main challenges in wind, wave and tidal energy data management, and to investigate and spearhead new ways of processing and handling data.
