Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/15/5638 |
| _version_ | 1797442235647655936 |
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| author | Sarah Barber Luiz Andre Moyses Lima Yoshiaki Sakagami Julian Quick Effi Latiffianti Yichao Liu Riccardo Ferrari Simon Letzgus Xujie Zhang Florian Hammer |
| author_facet | Sarah Barber Luiz Andre Moyses Lima Yoshiaki Sakagami Julian Quick Effi Latiffianti Yichao Liu Riccardo Ferrari Simon Letzgus Xujie Zhang Florian Hammer |
| author_sort | Sarah Barber |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T12:38:53Z |
| format | Article |
| id | doaj.art-70a814f776ab457cb93b4b4d7ec1739b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T12:38:53Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-70a814f776ab457cb93b4b4d7ec1739b2023-11-30T22:20:38ZengMDPI AGEnergies1996-10732022-08-011515563810.3390/en15155638Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case StudySarah Barber0Luiz Andre Moyses Lima1Yoshiaki Sakagami2Julian Quick3Effi Latiffianti4Yichao Liu5Riccardo Ferrari6Simon Letzgus7Xujie Zhang8Florian Hammer9Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, SwitzerlandVoltalia, 84 bd de Sébastopol, 75003 Paris, FranceFederal Institute of Santa Catarina, Av. Mauro Ramos 950, Florianópolis 88020-300, BrazilTurbulence and Energy Systems Laboratory, University of Colorado, Boulder, CO 80309, USADepartment of Industrial and Systems Engineering, Texas A & M University, College Station, TX 77843, USAElectric Power Research Institute (EPRI) Europe, NexusUCD, Block 9 & 10 Belfield Office Park, Beech Hill Road, D04 V2N9 Dublin, IrelandDelft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The NetherlandsMachine Learning Group, Technische Universität Berlin, Str. des 17. Juni 135, 10623 Berlin, GermanyFaculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaInstitute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, SwitzerlandIn 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.https://www.mdpi.com/1996-1073/15/15/5638wind energydigitalisationcollaborationco-innovationmachine learningfault detection |
| spellingShingle | Sarah Barber Luiz Andre Moyses Lima Yoshiaki Sakagami Julian Quick Effi Latiffianti Yichao Liu Riccardo Ferrari Simon Letzgus Xujie Zhang Florian Hammer Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study Energies wind energy digitalisation collaboration co-innovation machine learning fault detection |
| title | Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study |
| title_full | Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study |
| title_fullStr | Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study |
| title_full_unstemmed | Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study |
| title_short | Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study |
| title_sort | enabling co innovation for a successful digital transformation in wind energy using a new digital ecosystem and a fault detection case study |
| topic | wind energy digitalisation collaboration co-innovation machine learning fault detection |
| url | https://www.mdpi.com/1996-1073/15/15/5638 |
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