{"id":33897,"date":"2018-11-13T18:05:30","date_gmt":"2018-11-13T23:05:30","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/iberdrola-machine-learning-and-the-future-of-offshore-wind\/"},"modified":"2018-11-13T18:05:30","modified_gmt":"2018-11-13T23:05:30","slug":"iberdrola-machine-learning-and-the-future-of-offshore-wind","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/iberdrola-machine-learning-and-the-future-of-offshore-wind\/","title":{"rendered":"Iberdrola, machine learning and the future of offshore wind"},"content":{"rendered":"

Machine learning to tap into the potential of offshore wind power<\/strong><\/h2>\n

In 1887, inventor Charles Brush built a wind turbine in the backyard of his mansion in Cleveland Ohio. After generations of improvement by talented engineers, wind power is today one of the fastest-growing renewable energy technologies, encouraged by policy support, technology advances and financial incentives [1,2,3]. Offshore turbines specifically offer tremendous potential because of more consistent and higher wind speeds (improving yield) and fewer restrictions on size. Offshore wind capacity is expected to almost triple to 52 GW by 2023, with half the growth driven by the European Union and the other half by Asia [5]. Under conservative scenarios, this capacity should reach 160 GW by 2030 and 350 GW by 2040 [6].<\/p>\n

Iberdrola, global leader in power generation and one of the largest wind power producers, understands this potential. The group is investing heavily in renewables and in wind power specifically, to support the transition to low-carbon energy. In line with this strategy, its offshore wind capacity increased by 180% from 2016 to 2017 [7].<\/p>\n

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Figure 1 – Global levelized cost of electricity from utility-scale renewable power generation technologies, 2010-2017<\/em><\/figcaption><\/figure>\n

However, competitiveness compared to other technologies remains a key obstacle for offshore wind to fulfill this potential [8,9, figure 1]. Operations and Maintenance (O&M) costs account for 15% to 30 % of the Levelized Cost of Electricity (LCoE) and can be a strong lever of optimization [10 – 13]. Traditionally reactive, static and labor-intensive, offshore O&M includes costly unplanned maintenance and inspections \u2013 often in harsh conditions \u2013 and is expected to represent a $4.9 billion market by 2026 [14].<\/p>\n

This is where machine learning can improve the future of the industry. Using information generated by sensors in the turbines and learning from historical data, maintenance can become predictive. Anomalies and failure modes can be proactively and precisely identified to be addressed before causing any damage or service interruption [15 – 17]. Specific actions can be suggested to operators, switching from a calendar-based maintenance schedule to a much cheaper asset condition-based maintenance.<\/p>\n

Looking forward: Iberdrola\u2019s strategy to decrease offshore O&M costs<\/strong><\/h2>\n

Iberdrola is leading the ROMEO project (\u201cReliable OM decision tools and strategies for high LCoE reduction on offshore wind\u201d), working with 12 industrial partners including IBM, OEMs Adwen and Siemens [18,19]. The objective is to develop a platform to manage and analyze data collected from offshore wind turbines to:<\/p>\n

    \n
  • Increase reliability and decrease downtime due to equipment failure,<\/li>\n
  • Increase the lifespan of key turbine components,<\/li>\n
  • Reduce O&M costs through a smarter use of resources and decreased need of foundations inspections.<\/li>\n<\/ul>\n

    The outcomes of the project will contribute to the improvement of health and safety, to the development of operational best practices in offshore wind, leading to increased competitivity and lower LCOE.<\/p>\n