Sift AML (Accessible Machine Learning): Rapid and Robust Automated Analysis for Wind Farms
Lead Participant:
BITBLOOM LTD
Abstract
As the world turns away from fossil fuels, wind power will become an increasingly significant contributor to the global energy supply system in the coming decades. By harnessing the power of Machine Learning (ML) and Artificial Intelligence (AI), Sift AML transforms the mountains of data produced by wind farms to actionable knowledge that helps to increase their operating efficiency, reliability and maintenance. By doing so, they operate at a higher capacity for longer, saving us all money on our electricity bills.
Wind farms create vast quantities of data. A modern offshore wind farm may comprise a hundred or more wind turbines. Each turbine produces hundreds of data signals that contain crucial information about the state of each sub-system and the condition of the thousands of individual components. There are also several support systems that also produce data, such as substations, array cables, service vessels and meteorological masts.
In total, a modern offshore wind farm may generate in the order of 10 petabytes of 1Hz data and 15TB of 10-minute statistics annually. Hidden within these data is critical information about component wear and pending failures. When utilised correctly, these data can be transformed into knowledge that can help owners to improve efficiency and reliability, optimise maintenance regimes and reduce operating costs.
Most wind farms are connected to central data warehousing systems with web portals where the owners and operators can analyse the information for monitoring purposes. Generally, however, the capabilities of these systems to provide in-depth technical analysis and prognostics are very limited.
Sift AML is a simple to deploy, in-depth analytics and prognostics system that enables energy producers of any size to build advanced analytics on top of their existing data warehousing solutions. Using state-of-the-art developments in ML and AI, Sift AML provides access to these revolutionary technologies to everyone, not just data scientists and software programmers.
By using Sift AML wind turbine operators can gain rapid access to advanced analytics that turn data produced by plant machinery into actionable insights, including guidance on pending component failures and opportunities for increasing power production. In power production, even small increases in efficiency can generate significant returns at scale for operators, lowering the cost of electricity and protecting the planet by accelerating the shift toward carbon-neutral energy production across the world.
Wind farms create vast quantities of data. A modern offshore wind farm may comprise a hundred or more wind turbines. Each turbine produces hundreds of data signals that contain crucial information about the state of each sub-system and the condition of the thousands of individual components. There are also several support systems that also produce data, such as substations, array cables, service vessels and meteorological masts.
In total, a modern offshore wind farm may generate in the order of 10 petabytes of 1Hz data and 15TB of 10-minute statistics annually. Hidden within these data is critical information about component wear and pending failures. When utilised correctly, these data can be transformed into knowledge that can help owners to improve efficiency and reliability, optimise maintenance regimes and reduce operating costs.
Most wind farms are connected to central data warehousing systems with web portals where the owners and operators can analyse the information for monitoring purposes. Generally, however, the capabilities of these systems to provide in-depth technical analysis and prognostics are very limited.
Sift AML is a simple to deploy, in-depth analytics and prognostics system that enables energy producers of any size to build advanced analytics on top of their existing data warehousing solutions. Using state-of-the-art developments in ML and AI, Sift AML provides access to these revolutionary technologies to everyone, not just data scientists and software programmers.
By using Sift AML wind turbine operators can gain rapid access to advanced analytics that turn data produced by plant machinery into actionable insights, including guidance on pending component failures and opportunities for increasing power production. In power production, even small increases in efficiency can generate significant returns at scale for operators, lowering the cost of electricity and protecting the planet by accelerating the shift toward carbon-neutral energy production across the world.
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
Participant |
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| BITBLOOM LTD |
People |
ORCID iD |
| Philip Bradstock (Project Manager) |