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Maintenance Activities Coordination for Offshore Wind Farms Integrating Multivariate Stochastic Models

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Języki publikacji
EN
Abstrakty
EN
The efficiency and reliability of offshore wind farms are significantly influenced by their maintenance strategies. Effective maintenance coordination is crucial to minimize downtime and consequently maximize reliability. This paper proposes a methodology for integrating multivariate stochastic models into the maintenance scheduling process of offshore wind farms, enabling a more data-driven approach. The proposed framework accounts for the temporal and spatial dependencies of wind variations to optimize maintenance activities, ensuring minimal disruptions to energy production using the Expected Energy Not Supplied (EENS) indicator. We validate our approach using case studies that demonstrate its effectiveness through a sensitivity analysis. The results highlight more than 6% deviations in the EENS estimate for scenarios with the same conditions, where the only difference is the wind speed simulation approach. The change in approach impacts the overall allocation of maintenance activities to offshore wind farms.
Twórcy
  • AGH University of Kraków, Kraków, Poland
  • AGH University of Kraków, Kraków, Poland
Bibliografia
  • [1] Namkyoung Lee, Joohyun Woo, Sungryul Kim. A deep reinforcement learning ensemble for maintenance scheduling in offshore wind farms, Applied Energy, Volume 377, Part A, 2025, 124431, ISSN 0306-2619.
  • [2] Xiangyong Feng, Shunjiang Lin, Yutao Liang, Xin Lai, Mingbo Liu. Coordinated risk-averse distributionally robust optimization for maintenance and generation schedules of offshore wind farm cluster, International Journal of Electrical Power & Energy Systems, Volume 159, 2024, 109993, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2024.109993.
  • [3] Salgado Duarte Y, Szpytko J, Reliability-oriented twin model for integrating offshore wind farm maintenance activities, Eksploatacja i Niezawodnosc – Maintenance and Reliability 2025: 27(3) http://doi.org/10.17531/ein/199355.
  • [4] Markus Steen, Tuukka Mäkitie, Jens Hanson, Håkon Endresen Normann, Developing the industrial capacity for energy transitions: Resource formation for offshore wind in Europe, Environmental Innovation and Societal Transitions, Volume 53, 2024, 100925, ISSN 2210-4224, https://doi.org/10.1016/j.eist.2024.100925.
  • [5] Rui He, Zhigang Tian, Yifei Wang, Yinuo Chen, Ming J. Zuo, Predictive maintenance for offshore wind farms with incomplete and biased prognostic information, Ocean Engineering, Volume 322, 2025, 120541, ISSN 0029-8018, https://doi.org/10.1016/j.oceaneng.2025.120541.
  • [6] Jiaxuan Luo, Xiaofang Luo, Xiandong Ma, Yingfei Zan, Xu Bai, An integrated condition-based opportunistic maintenance framework for offshore wind farms, Reliability Engineering & System Safety, Volume 256, 2025, 110701, ISSN 0951-8320, https://doi.org/10.1016/j.ress.2024.110701.
  • [7] Shuya Zhong, Athanasios A. Pantelous, Mark Goh, Jian Zhou, A reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms, Mechanical Systems and Signal Processing, Volume 124, 2019, Pages 643-663, ISSN 0888-3270, https://doi.org/10.1016/j.ymssp.2019.02.012.
  • [8] Papadopoulos, P., Coit, D. W., & Aziz Ezzat, A. (2023). STOCHOS: Stochastic opportunistic maintenance scheduling for offshore wind farms. IISE Transactions, 56(1), 1–15. https://doi.org/10.1080/24725854.2022.2152913.
  • [9] Qian Sun, Jinxing Che, Kun Hu, Wen Qin, Deterministic and probabilistic wind speed forecasting using decomposition methods: Accuracy and uncertainty, Renewable Energy, Volume 243, 2025, 122515, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2025.122515.
  • [10] Xiaolin Ge, Quan Chen, Yang Fu, C.Y. Chung, Yang Mi, Optimization of maintenance scheduling for offshore wind turbines considering the wake effect of arbitrary wind direction, Electric Power Systems Research, Volume 184, 2020, 106298, ISSN 0378-7796, https://doi.org/10.1016/j.epsr.2020.106298.
  • [11] Christian Gundegjerde, Ina B. Halvorsen, Elin E. Halvorsen-Weare, Lars Magnus Hvattum, Lars Magne Nonås, A stochastic fleet size and mix model for maintenance operations at offshore wind farms, Transportation Research Part C: Emerging Technologies, Volume 52, 2015, Pages 74-92, ISSN 0968-090X, https://doi.org/10.1016/j.trc.2015.01.005.
  • [12] S. Astariz, J. Abanades, C. Perez-Collazo, G. Iglesias, Improving wind farm accessibility for operation & maintenance through a co-located wave farm: Influence of layout and wave climate, Energy Conversion and Management, Volume 95, 2015, Pages 229-241, ISSN 0196-8904, https://doi.org/10.1016/j.enconman.2015.02.040.
  • [13] Jack C Kennedy, Daniel A Henderson, Kevin J Wilson, Multilevel emulation for stochastic computer models with application to large offshore wind farms, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 72, Issue 3, June 2023, Pages 608–627, https://doi.org/10.1093/jrsssc/qlad023.
  • [14] The MathWorks Inc. MATLAB Version: 24.1.0.2603908 (R2024a) Update 3, Natick, Massachusetts 2024. The MathWorks Inc. https://www.mathworks.com.
  • [15] Grigg C. et al. The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee. IEEE Transactions on Power Systems 1999; 14(3): 1010-1020, https://doi: 10.1109/59.780914.
  • [16] Global Offshore Wind Report. (2024) https://wfo-global.org/wp-content/uploads/2024/04/WFO-Report-2024Q1.pdf
Uwagi
Pełne imiona podano na stronie internetowej czasopisma w "Authors in other databases."
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8e44c13c-2c5d-46a9-aba8-57735790dc9f
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