Dynamic Dispatching Strategy for Maintenance Tasks of Offshore Wind Farm

Yang FU; Yaonan ZHANG; Lujie LIU; Shurong WEI; Haohan REN

doi:10.7500/AEPS20210420007

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Dynamic Dispatching Strategy for Maintenance Tasks of Offshore Wind Farm

Research and Application of Key Technologies for Large-scale Offshore Wind Power System | 更新时间：2025-06-16

- Dynamic Dispatching Strategy for Maintenance Tasks of Offshore Wind Farm
- Automation of Electric Power Systems Vol. 45, Issue 21, Pages: 48-56(2021)
- 作者机构：
  
  1.上海电力大学电气工程学院，上海市 200090
  2.上海绿色环保能源有限公司，上海市 200433
- 作者简介：
- 基金信息：
  
  State Grid Corporation of China(4000-201999350A-0-0-00)
- DOI：10.7500/AEPS20210420007
  CLC：
- Received：20 April 2021，
  
  Revised：2021-08-11，
  
  Published Online：30 September 2021，
  
  Published：10 November 2021
- 稿件说明：
移动端阅览
符杨,张耀楠,刘璐洁等.海上风电场维护任务动态调度策略[J].电力系统自动化,2021,45(21):48-56.

FU Yang,ZHANG Yaonan,LIU Lujie,et al.Dynamic Dispatching Strategy for Maintenance Tasks of Offshore Wind Farm[J].Automation of Electric Power Systems,2021,45(21):48-56.
符杨,张耀楠,刘璐洁等.海上风电场维护任务动态调度策略[J].电力系统自动化,2021,45(21):48-56. DOI： 10.7500/AEPS20210420007.

FU Yang,ZHANG Yaonan,LIU Lujie,et al.Dynamic Dispatching Strategy for Maintenance Tasks of Offshore Wind Farm[J].Automation of Electric Power Systems,2021,45(21):48-56. DOI： 10.7500/AEPS20210420007.

摘要

在海上风浪、载荷等因素的耦合作用下，风机状态数据波动迅速，时变工况下风机状态特征的敏感性导致维护需求的动态变化，增加了风电场维护任务精准调度的难度。文中提出了海上时变工况下考虑风机状态风险态势的风电场维护任务动态调度方法。首先，利用模糊

均值聚类算法划分风机时变工况，通过采用改进联合领域自适应卷积神经网络最小化特征分布差异，实现时变工况下风机状态特征自适应提取。然后，根据部件状态序列利用马尔可夫模型描述各部件的初始状态转移矩阵，考虑到不完全维护对机组部件性能的影响，引入部件性能退化过程，建立了考虑风机自适应状态评估的风险态势预测模型。同时，提出以维护船只、人员、工作时长等条件为约束，以单位电量调度维护成本最小为目标的海上风电场维护任务动态调度方法，实现了时变工况下海上风电场维护任务的动态调度。最后，以某海上风电场为例，验证了所提方法的有效性和经济性。

Abstract

Under the coupling effect of offshore wind

wave

mechanical load and other factors

the state data of wind turbines fluctuates rapidly. The sensitivity of wind turbine state characteristics under time-varying conditions leads to the dynamic change of wind turbine maintenance requirements

which increases the difficulty in accurately dispatching maintenance tasks of wind farm. This paper proposes a dynamic dispatching method for maintenance tasks of wind farm considering the risk situation of wind turbine state under time-varying sea conditions. First

the fuzzy

-means clustering algorithm is used to divide the time-varying working conditions of wind turbines. The improved joint-domain adaptive convolutional neural network is used to minimize the difference of feature distribution

so as to realize the adaptive extraction of the wind turbine condition features under time-varying working conditions. Then

the Markov model is used to describe the initial state transition matrix of each component. Considering the influence of incomplete maintenance on the performance of the unit components

the state risk situation prediction model considering the adaptive state assessment of the wind turbine is established by the component performance degradation model. On this basis

with the constraints of maintenance vessels

personnel and working hours

a dynamic dispatching model of maintenance tasks of offshore wind farm is established to minimize the cost of unit power dispatching and maintenance dispatching

which realizes the dynamic dispatching for maintenance tasks of offshore wind farm under time-varying conditions. Finally

the effectiveness and economy of the proposed method is verified by taking an offshore wind farm as an example.

关键词

Keywords

references

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