针对海上风电场维护任务精准调度的需求,利用三参数威布尔分布模型计算风电机组关键零部件的可靠度函数,以确定其最佳维护时间窗,并采用混合策略的离散灰狼算法（HGWO）进行运维方案的优化。首先,考虑过度维修和维修不足的运维时间窗违规次数,建立包含时间窗违规惩罚成本的海上风电机组运维方案优化模型;其次,基于HGWO求解最小化运维成本的问题。最后,以某海上风电场实际运维案例数据对模型算法进行验证,并与遗传算法等其他传统方法进行比较,实验结果表明HGWO在求解时间窗约束下的海上风电维护方案优化问题具有收敛速度快、寻优效率高的优势,相比于遗传算法等传统算法在求解该问题时表现出更强的全局搜索能力,验证了所提方法的有效性和经济性。
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.
关键词
海上风电 /
维护计划 /
威布尔分布 /
离散灰狼算法 /
运维时间窗
Key words
offshore wind power /
maintenance schedules /
Weibull distribution /
discrete grey wolf algorithm /
operation and maintenance time window
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基金
贵州省科技支撑计划（黔科合支撑[2023]一般411; 黔科合支撑[2024]一般051）; 贵州省教育厅工程研究中心（黔教技[2022]043）; 中国电建集团科技项目（DJ-ZDXM-2022-44）
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