Articles & Issues
- Language
- English
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received June 4, 2024
Accepted September 16, 2024
- Acknowledgements
- Wind power generation · Wind power fl uctuation · k-means clustering · Swing door algorithm · Stress testing
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Identifi cation of Representative Wind Power Fluctuation Patterns for Water Electrolysis Device Stress Testing: A Data Mining Approach
Abstract
Wind power generation is expected to greatly contribute to the future of humanity as a promising source of renewable
energy. However, the high variability inherent in wind is a challenge that hinders stable power generation. To utilize wind
power as a primary energy source, integration with a polymer electrolyte membrane water electrolysis (PEMWE) system
is proposed. Yet, PEMWE is known to suff er from degradation when exposed to input power patterns with high variability.
This poses challenges to its commercialization. This necessitates stress testing with various wind power fl uctuations during
the production process of the devices. This study investigates representative patterns of wind power fl uctuation so that these
patterns can be used for the stress testing process. We employ data-mining techniques, including the swing door algorithm
and k-means clustering, to identify these patterns by analyzing wind power generation data at a 10-s interval. As a result,
the fi ve most representative wind power ramps are presented. This study provides practical guidelines for the development
process of expensive devices for wind power generation, thereby promoting the active utilization of wind power generation.
