OPTIMIZATION STRATEGY FOR OFFSHORE WIND FARM MAINTENANCE WITHIN OPTIMAL OPERATION AND MAINTENANCE TIME WINDOW

Qu Chong, Yan Jianguo, Qin Tao, Liu Ying, Yang Jing

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 141-150.

PDF(2017 KB)
Welcome to visit Acta Energiae Solaris Sinica, Today is
PDF(2017 KB)
Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 141-150. DOI: 10.19912/j.0254-0096.tynxb.2024-0672

OPTIMIZATION STRATEGY FOR OFFSHORE WIND FARM MAINTENANCE WITHIN OPTIMAL OPERATION AND MAINTENANCE TIME WINDOW

  • Qu Chong1, Yan Jianguo2, Qin Tao1, Liu Ying3, Yang Jing1,4
Author information +
History +

Abstract

To address the precise scheduling requirements of offshore wind farm maintenance tasks, a three-parameter Weibull model was employed to compute the reliability functions of critical wind turbine components and ascertain the optimal maintenance time window. A hybrid strategy discrete grey wolf optimization(HGWO) algorithm was utilized to optimize the maintenance plan. Firstly, considering the number of violations in the operation and maintenance time window due to excessive maintenance and insufficient maintenance, an optimization model for the operation and maintenance plan of offshore wind turbines that includes the penalty cost for violations in the time window is established. Subsequently, the optimization problem of minimizing maintenance costs was addressed using the HGWO algorithm. Finally, the model and algorithm were validated using empirical maintenance data from an offshore wind farm and benchmarked against traditional methods, such as genetic algorithms. Experimental results indicat that the HGWO algorithm exhibits rapid convergence and high search efficiency in addressing the offshore wind farm maintenance optimization problem under time window constraints. In comparison to traditional algorithms such as genetic algorithms, the proposed method demonstrates superior global search capabilities, thus corroborating its effectiveness and economic efficiency.

Key words

offshore wind power / maintenance schedules / Weibull distribution / discrete grey wolf algorithm / operation and maintenance time window

Cite this article

Download Citations
Qu Chong, Yan Jianguo, Qin Tao, Liu Ying, Yang Jing. OPTIMIZATION STRATEGY FOR OFFSHORE WIND FARM MAINTENANCE WITHIN OPTIMAL OPERATION AND MAINTENANCE TIME WINDOW[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 141-150 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0672

References

[1] DA FONSECA SANTIAGO R A, BARBOSA N B, MERGULHÃO H G, et al. Data-driven models applied to predictive and prescriptive maintenance of wind turbine: a systematic review of approaches based on failure detection, diagnosis, and prognosis[J]. Energies, 2024, 17(5): 1010.
[2] LEE J.The status of wind in africa report 2023[R]. Brussels, Belgium: Global Wind Energy Council, 2023.
[3] 赵群, 柴福莉. 海上风力发电现状与发展趋势[J]. 机电工程, 2009, 26(12): 5-8.
ZHAO Q, CHAI F L.Current status and trend of the offshore wind power[J]. Journal of mechanical & electrical engineering, 2009, 26(12): 5-8.
[4] 张尉. 江苏中部沿海海上风电运维方案研究[D]. 南京: 南京理工大学, 2018.
ZHANG W.Research on operation and maintenance scheme of offshore wind power in the central coastal area of Jiangsu Province[D]. Nanjing: Nanjing University of Science and Technology, 2018.
[5] 葛畅, 阎洁, 刘永前, 等. 海上风电场运行控制维护关键技术综述[J]. 中国电机工程学报, 2022, 42(12): 4278-4291.
GE C, YAN J, LIU Y Q, et al.Review of key technologies for operation control and maintenance of offshore wind farm[J]. Proceedings of the CSEE, 2022, 42(12): 4278-4291.
[6] FENG Q, BI W J, CHEN Y R, et al.Cooperative game approach based on agent learning for fleet maintenance oriented to mission reliability[J]. Computers & industrial engineering, 2017, 112: 221-230.
[7] IRAWAN C A, OUELHADJ D, JONES D, et al.Optimisation of maintenance routing and scheduling for offshore wind farms[J]. European journal of operational research, 2017, 256(1): 76-89.
[8] 唐宏芬, 王丽杰, 吴春, 等. 考虑出航时间窗的海上风电场运维调度建模研究[J]. 电工技术, 2022(3): 145-148.
TANG H F, WANG L J, WU C, et al.Research on modeling of offshore wind farm operation and maintenance scheduling considering sailing time window[J]. Electric engineering, 2022(3): 145-148.
[9] ALLAL A, SAHNOUN M, ADJOUDJ R, et al.Multi-agent based simulation-optimization of maintenance routing in offshore wind farms[J]. Computers & industrial engineering, 2021, 157: 107342.
[10] STÅLHANE M, HVATTUM L M, SKAAR V. Optimization of routing and scheduling of vessels to perform maintenance at offshore wind farms[J]. Energy procedia, 2015, 80: 92-99.
[11] 谢鲁冰. 海上风电机组动态机会成组维修策略的研究[D]. 北京: 华北电力大学, 2021.
XIE L B.Research on dynamic opportunistic group maintenance strategy of offshore wind turbines[D].Beijing: North China Electric Power University, 2021.
[12] 符杨, 黄路遥, 刘璐洁, 等. 基于状态自适应评估的海上风电机组预防性维护策略[J]. 电力自动化设备, 2022, 42(1): 1-9.
FU Y, HUANG L Y, LIU L J, et al.Preventive maintenance strategy for offshore wind turbine based on state adaptive assessment[J]. Electric power automation equipment, 2022, 42(1): 1-9.
[13] 符杨, 许伟欣, 刘璐洁, 等. 考虑天气因素的海上风电机组预防性机会维护策略优化方法[J]. 中国电机工程学报, 2018, 38(20): 5947-5956.
FU Y, XU W X, LIU L J, et al.Optimization of preventive opportunistic maintenance strategy for offshore wind turbine considering weather conditions[J]. Proceedings of the CSEE, 2018, 38(20): 5947-5956.
[14] 董文康, 吴雨芯, 姚琦, 等. 基于深度强化学习的海上风电机组状态维护与备件库存联合优化[J]. 太阳能学报, 2023, 44(12): 190-199.
DONG W K, WU Y X, YAO Q, et al.Joint optimization of state maintenance and spare parts inventory of offshore wind turbines based on deep reinforcement learning[J]. Acta energiae solaris sinica, 2023, 44(12): 190-199.
[15] ZHANG J Q, CHOWDHURY S, ZHANG J, et al.Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses[J]. Wind energy, 2023, 26(11): 1103-1122.
[16] WANG Y, DENG Q R.Optimization of maintenance scheme for offshore wind turbines considering time windows based on hybrid ant colony algorithm[J]. Ocean engineering, 2022, 263: 112357.
[17] 吕致为, 王永, 邓奇蓉. 考虑时间窗约束的海上风电机组运维方案优化[J]. 太阳能学报, 2022, 43(10): 177-185.
LYU Z W, WANG Y, DENG Q R.Optimization of maintenance scheme for offshore wind turbines considering time window[J]. Acta energiae solaris sinica, 2022, 43(10): 177-185.
[18] MIRJALILI S, MIRJALILI S M, LEWIS A.Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.
[19] BAI J F, LI Y F, ZHENG M P, et al.A sinh cosh optimizer[J]. Knowledge-based systems, 2023, 282: 111081.
[20] 黄海悦. 基于零件可靠度的海上风电机组机会维护策略[D]. 北京: 华北电力大学, 2018.
HUANG H Y.Opportunity maintenance strategy of offshore wind turbine based on component reliability[D]. Beijing: North China Electric Power University, 2018.
[21] 张琛, 郭盛, 高伟, 等. 基于可靠度的风电机组机会维修策略[J]. 广东电力, 2016, 29(2): 40-44.
ZHANG C, GUO S, GAO W, et al.Opportunistic maintenance strategy for wind power generators based on reliability[J]. Guangdong electric power, 2016, 29(2): 40-44.
[22] 宋明阳, 瞿晟珉, 秦少茜, 等. 基于故障风险水平的海上风电场机会维护策略[J]. 电力工程技术, 2023, 42(6): 117-129.
SONG M Y, QU S M, QIN S X, et al.Offshore wind farm opportunity maintenance strategy based on failure risk level[J]. Electric power engineering technology, 2023, 42(6): 117-129.
PDF(2017 KB)

Accesses

Citation

Detail

Sections
Recommended

/