LAPSE:2023.11062
Published Article
LAPSE:2023.11062
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, Florian Hammer
February 27, 2023
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
co-innovation, collaboration, digitalisation, Fault Detection, Machine Learning, wind energy
Suggested Citation
Barber S, Lima LAM, Sakagami Y, Quick J, Latiffianti E, Liu Y, Ferrari R, Letzgus S, Zhang X, Hammer F. Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study. (2023). LAPSE:2023.11062
Author Affiliations
Barber S: Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland [ORCID]
Lima LAM: Voltalia, 84 bd de Sébastopol, 75003 Paris, France
Sakagami Y: Federal Institute of Santa Catarina, Av. Mauro Ramos 950, Florianópolis 88020-300, Brazil [ORCID]
Quick J: Turbulence and Energy Systems Laboratory, University of Colorado, Boulder, CO 80309, USA [ORCID]
Latiffianti E: Department of Industrial and Systems Engineering, Texas A & M University, College Station, TX 77843, USA; Institut Teknologi Sepuluh Nopember, Surabaya 60111, Jawa Timur, Indonesia [ORCID]
Liu Y: Electric Power Research Institute (EPRI) Europe, NexusUCD, Block 9 & 10 Belfield Office Park, Beech Hill Road, D04 V2N9 Dublin, Ireland [ORCID]
Ferrari R: Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands [ORCID]
Letzgus S: Machine Learning Group, Technische Universität Berlin, Str. des 17. Juni 135, 10623 Berlin, Germany
Zhang X: Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China
Hammer F: Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland
Journal Name
Energies
Volume
15
Issue
15
First Page
5638
Year
2022
Publication Date
2022-08-03
Published Version
ISSN
1996-1073
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PII: en15155638, Publication Type: Journal Article
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LAPSE:2023.11062
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doi:10.3390/en15155638
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Feb 27, 2023
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