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

Sarah Barber, Luiz Andre Moyses Lima, Yoshiaki Sakagami, Julian Quick, Effi Latiffianti, Yichao Liu, Riccardo Ferrari, Simon Letzgus, Xujie Zhang and Florian Hammer
Additional contact information
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
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

Energies, 2022, vol. 15, issue 15, 1-32

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.

Keywords: wind energy; digitalisation; collaboration; co-innovation; machine learning; fault detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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