海上风电机组运维排程优化模型及其改进遗传算法

周鹏飞; 刘俊

doi:10.19912/j.0254-0096.tynxb.2024-2328

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海上风电机组运维排程优化模型及其改进遗传算法

更新时间：2026-07-21

- 海上风电机组运维排程优化模型及其改进遗传算法
- Vol. 47, Issue 5, Pages: 247-257(2026)
- 作者机构：
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- DOI：10.19912/j.0254-0096.tynxb.2024-2328
  CLC： TK83
- Online First：04 June 2026，
  
  Published：2026
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周鹏飞, 刘俊. 海上风电机组运维排程优化模型及其改进遗传算法[J]. 2026, 47(5): 247-257.
DOI：

周鹏飞, 刘俊. 海上风电机组运维排程优化模型及其改进遗传算法[J]. 2026, 47(5): 247-257. DOI： 10.19912/j.0254-0096.tynxb.2024-2328.

摘要

针对海上风电场复杂运维任务优化排程问题

提出一种任务风电机组运维排程优化模型和改进遗传算法（OPGA）。该模型考虑了运维班组任务负荷与工时利用效率

引入任务风电机组组合策略

通过优化运维作业次序与路径

实现运维成本的最小化以及作业效率的提升;约束条件包括运维船载重、航距以及班组工时限制等;为提高模型求解精度与效率

OPGA算法采用不重复自然数编码表征运维作业次序路径

在解码过程中嵌入分支定界搜索方法

并引入种群灾变优化操作。以某海上风电场工程为背景的试验结果表明

OPGA较传统遗传算法求解较大规模问题的运维成本节约15%以上

工时利用率提升10%以上。

Abstract

To address the intricate challenge of scheduling optimization for maintenance tasks in offshore wind farms

an innovative optimization model for offshore wind turbine O&M scheduling and an enhanced genetic algorithm

termed Optimized Genetic Algorithm （OPGA）

were introduced. This model takes into account the workload and operational efficiency of the maintenance teams

incorporates a strategic approach for task-turbine grouping

and refines the sequence and route of maintenance activities with the aim of reducing maintenance costs and enhancing operational productivity. The model's constraints encompass the maintenance vessel's load capacity and travel distance

the team's working time constraints

among others. In order to augment the accuracy and efficiency of the model

the OPGA algorithm employs non-repeating natural-number encoding to depict the sequence and path of maintenance operations

integratesa branch-and-bound search within the decoding phase

and incorporates a population-catastrophe operator. The experimental results based on an offshore wind farm project show that compared with the traditional genetic algorithm

OPGA can save more than 15% of the operation and maintenance cost and increase the working hour utilization rate by more than 10% when solving largescale problems.

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references

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