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Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study

Author

Listed:
  • Sarah Barber

    (Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland)

  • Luiz Andre Moyses Lima

    (Voltalia, 84 bd de Sébastopol, 75003 Paris, France)

  • Yoshiaki Sakagami

    (Federal Institute of Santa Catarina, Av. Mauro Ramos 950, Florianópolis 88020-300, Brazil)

  • Julian Quick

    (Turbulence and Energy Systems Laboratory, University of Colorado, Boulder, CO 80309, USA)

  • Effi Latiffianti

    (Department of Industrial and Systems Engineering, Texas A & M University, College Station, TX 77843, USA
    Institut Teknologi Sepuluh Nopember, Surabaya 60111, Jawa Timur, Indonesia)

  • Yichao Liu

    (Electric Power Research Institute (EPRI) Europe, NexusUCD, Block 9 & 10 Belfield Office Park, Beech Hill Road, D04 V2N9 Dublin, Ireland)

  • Riccardo Ferrari

    (Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands)

  • Simon Letzgus

    (Machine Learning Group, Technische Universität Berlin, Str. des 17. Juni 135, 10623 Berlin, Germany)

  • Xujie Zhang

    (Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Florian Hammer

    (Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland)

Abstract

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method based on a digital ecosystem is developed and demonstrated. The method is centred around specific “challenges”, which are defined by “challenge providers” within a topical “space” and made available to participants via a digital platform. The data required in order to solve a particular “challenge” are provided by the “challenge providers” under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the solutions perform significantly better than EDP’s existing solution in terms of Total Prediction Costs (saving up to €120,000). The digital ecosystem is found to be a promising solution for enabling co-innovation in wind energy in general, providing a number of tangible benefits for both challenge and solution providers.

Suggested Citation

  • Sarah Barber & Luiz Andre Moyses Lima & Yoshiaki Sakagami & Julian Quick & Effi Latiffianti & Yichao Liu & Riccardo Ferrari & Simon Letzgus & Xujie Zhang & Florian Hammer, 2022. "Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study," Energies, MDPI, vol. 15(15), pages 1-32, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5638-:d:879476
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    References listed on IDEAS

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    Cited by:

    1. Sarah Barber & Unai Izagirre & Oscar Serradilla & Jon Olaizola & Ekhi Zugasti & Jose Ignacio Aizpurua & Ali Eftekhari Milani & Frank Sehnke & Yoshiaki Sakagami & Charles Henderson, 2023. "Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation," Energies, MDPI, vol. 16(8), pages 1-23, April.
    2. Arranz, Carlos F.A. & Arroyabe, Marta F. & Arranz, Nieves & de Arroyabe, Juan Carlos Fernandez, 2023. "Digitalisation dynamics in SMEs: An approach from systems dynamics and artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 196(C).

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