计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 795-801.doi: 10.11896/jsjkx.210400300

• 交叉&应用 • 上一篇    下一篇

海上风电场通用运维路径规划模型优化及仿真

谭任深1,2, 徐龙博1, 周冰1, 荆朝霞2, 黄向生3   

  1. 1 中国能源建设集团广东省电力设计研究院有限公司 广州 510663
    2 华南理工大学电力学院 广州 510640
    3 中国科学院自动化研究所 北京 100190
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 谭任深(tanrenshen@gedi.com.cn)
  • 基金资助:
    2019年度广东省促进经济发展专项基金(海洋经济发展用途):海上风电智能运维策略研究(GDOE[2019]A10号)

Optimization and Simulation of General Operation and Maintenance Path Planning Model for Offshore Wind Farms

TAN Ren-shen1,2, XU Long-bo1, ZHOU Bing1, JING Zhao-xia2, HUANG Xiang-sheng3   

  1. 1 China Energy Engineering Group Guangdong Electric Power Design Institute Co.,Ltd.,Guangzhou 510663,China
    2 School of Electric Power Engineering,South China University of Technology,Guangzhou 510640,China
    3 Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:TAN Ren-shen,born in 1988,Ph.D candidate,senior engineer.His main research interests include offshore windfarm intelligent operation and maintenance and artificial intelligence techno-logy for offshore wind Power and economic evaluation of offshore windfarm.
  • Supported by:
    2019 Guangdong Provincial Special Fund for Economic Development(Marine Economic Development Purposes) “Offshore Wind Power Intelligent Operation and Maintenance Strategy Research”(GDOE[2019]A10号).

摘要: 海上风电场运维的路径规划是一项极具挑战性和复杂性的任务,需要确定运维所需要的资源、交通工具路径,使得总运维成本最小化。文中在海上风电场运维规划建模方面采用了抽象类的方式,建立了通用性运维路径规划模型框架,该模型有利于兼容不同的海上风电场运维的路径规划与调度决策任务,可提高模型的可扩展性和多场景适用的灵活性。采用改进的自适应大邻域搜索算法(ALNS),提出在含有多个destroy和repair算子的算法基础上,求解基于抽象类的通用运维路径规划模型。选定国内某风电场数据进行仿真实验,通过在ALNS内部对比6个算子求解的结果,以及将ALNS与精确算法结果进行比较,结果显示该算法具有较好的优化效果和可靠性。

关键词: 抽象类, 海上风电, 路径规划, 通用运维规划模型, 自适应大邻域搜索

Abstract: The path planning of offshore wind farm operation and maintenance is a challenging and complex task,which needs to determine the resources and transport paths needed by the operation and maintenance,so as to minimize the total operation and maintenance cost.In this paper,the abstract class method is adopted in the modeling of offshore wind farm operation and maintenance planning,and a general operation and maintenance path planning model framework is established.This model is conducive to the compatibility of different offshore wind farm operation and maintenance path planning and scheduling decision-making tasks.improve the scalability of the model and the flexibility of multi-scenario application.In this paper,an improved adaptive large neighborhood search algorithm (ALNS),is proposed to solve the general operation and maintenance path planning model based on abstract class on the basis of the algorithm with multiple destroy and repair operators.Finally,the data of a domestic wind farm is selected for simulation experiment.By comparing the results of six operators within ALNS,and comparing the results of ALNS with the results of accurate algorithm,the results show that the algorithm optimization has better effect and reliability.

Key words: Abstract class, Adaptive large neighborhood search, General operation and maintenance planning model, Offshore windfarm, Path planning

中图分类号: 

  • TP181
[1] LIU Y Q,MA Y C,TAO T.Research status and prospects of maintenance and management technology for offshore wind farms[J].Global Energy Internet,2019,2(2):127-137.
[2] Foreseeing 2021:Panorama of the industrial chain of wind power operation and maintenance industry[J].China Electrical Equipment Industry,2021(3):18-21.
[3] GWEC Report on Wind Power[R/OL].Africa Research Bulletin:Economic,Financial and Technical Series,2020.https://onlinelibrary.wiley.com/doi/10.1111/j.1467-6346.2020.09409.x.
[4] WANG Z Y.The vision of carbon neutrality catalyzes the birth of new targets for the “landscape” industry[J].China Investment (Chinese and English),2021(Z1):20-22.
[5] CARROLL J,MCDONALD A,DINWOODIE I,et al.Availabi-lity,operation and maintenance costs of offshore wind turbines with different drive train configurations[J].Wind Energy,2017,20(2):361-378.
[6] BIDWELL D.Ocean beliefs and support for an offshore windenergy project[J].Ocean & Coastal Management,2017(146):99-108.
[7] ZHANG J,GAO C,XU B,et al.Research on the electrical design of a new type of flexible DC energy consumption device for offshore wind power DC grid connection projects[J/OL].Proceedings of the Chinese Society for Electrical Engineering.https://kns-cnki-net-443.webvpn.scut.edu.cn/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2021&file-name=ZGDC202112008&uniplatform=NZKPT&v=3UDY-INjhgr__lBUyZIIuqJaPtZbuXrUxnU45667DaTlUF0aFnnKpBIGHt6qjdhx.
[8] LIU L,FU Y,MA S W,et al.Preventive Maintenance Strategy for offshore wind turbine based on reliability and maintenance priority[J].Proceedings of the CSEE,2016,36(21):5732-5740.
[9] WAN Y C,WANG K,CHU Y F.Summary of the technical status and development of offshore wind power operation and maintenance[J].Ship Engineering,2020,42(12):20-25.
[10] WANG Y,HAN B,ZHAO W C,et al.Establishment and analysis of economic evaluation model for offshore wind power emergency maintenance[J].Ship Engineering,2020,42(S1):605-608,611.
[11] RUI X M,XIE L B,LI S,et al.Research on the maintenance strategy of offshore wind turbines for accessibility[J].Journal of North China Electric Power University (Natural Science Edition),2019,46(5):92-99.
[12] STLHANE M,HVATTUM L M,SKAAR V.Optimization ofRouting and Scheduling of Vessels to Perform Maintenance at Offshore Wind Farms[J].Energy Procedia,2015,80:92-99.
[13] ZHANG Z.Multi-ACO Application in Routing and SchedulingOptimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms[J].Journal of Power and Energy Engineering,2018,6(10):20-40.
[14] CAI A,DO A,DJ A,et al.Optimisation of maintenance routing and scheduling for offshore wind farms[J].European Journal of Operational Research,2017,256(1):76-89.
[15] RAKNES N T,ØDESKAUG K,STÅLHANE M,et al.Scheduling of Maintenance Tasks and Routing of a Joint Vessel Fleet for Multiple Offshore Wind Farms[J].Journal of Marine Science and Engineering,2017,5(1):11.
[16] CHROTENBOER A H,BROEK M,JARGALSAIKHAN B,et al.Coordinating technician allocation and maintenance routing for offshore wind farms[J].Computers & Operations Research,2018,98(OCT.):185-197.
[17] STOCK-WILLIAMS C,SWAMY S K.Automated daily maintenance planning for offshore wind farms[J].Renewable Energy,2019,133(APR.):1393-1403.
[18] IRAWAN C A,ESKANDARPOUR M,OUELHADJ D,et al.Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm[J].European Journal of Operational Research,2019,289(3):912-926.
[1] 王兵, 吴洪亮, 牛新征.
基于改进势场法的机器人路径规划
Robot Path Planning Based on Improved Potential Field Method
计算机科学, 2022, 49(7): 196-203. https://doi.org/10.11896/jsjkx.210500020
[2] 杨浩雄, 高晶, 邵恩露.
考虑一单多品的外卖订单配送时间的带时间窗的车辆路径问题
Vehicle Routing Problem with Time Window of Takeaway Food ConsideringOne-order-multi-product Order Delivery
计算机科学, 2022, 49(6A): 191-198. https://doi.org/10.11896/jsjkx.210400005
[3] 沈彪, 沈立炜, 李弋.
空间众包任务的路径动态调度方法
Dynamic Task Scheduling Method for Space Crowdsourcing
计算机科学, 2022, 49(2): 231-240. https://doi.org/10.11896/jsjkx.210400249
[4] 陈镜宇, 郭志军, 尹亚昆.
基于混合算法的智能割草机全遍历路径规划及其系统设计
Full Traversal Path Planning and System Design of Intelligent Lawn Mower Based on Hybrid Algorithm
计算机科学, 2021, 48(6A): 633-637. https://doi.org/10.11896/jsjkx.201100002
[5] 杜婉茹, 王潇茵, 田涛, 张越.
面向未知环境及动态障碍的人工势场路径规划算法
Artificial Potential Field Path Planning Algorithm for Unknown Environment and Dynamic Obstacles
计算机科学, 2021, 48(2): 250-256. https://doi.org/10.11896/jsjkx.191100170
[6] 郭启程, 杜晓玉, 张延宇, 周毅.
基于改进鲸鱼算法的无人机三维路径规划
Three-dimensional Path Planning of UAV Based on Improved Whale Optimization Algorithm
计算机科学, 2021, 48(12): 304-311. https://doi.org/10.11896/jsjkx.201000021
[7] 赵杨, 倪志伟, 朱旭辉, 刘浩, 冉家敏.
基于改进狮群进化算法的面向空间众包平台的多工作者多任务路径规划方法
Multi-worker and Multi-task Path Planning Based on Improved Lion Evolutionary Algorithm forSpatial Crowdsourcing Platform
计算机科学, 2021, 48(11A): 30-38. https://doi.org/10.11896/jsjkx.201200085
[8] 曹波, 陈锋, 成静, 李华, 李永乐.
基于全向路口模型的非结构化道路重复节点路径规划
Route Planning of Unstructured Road Including Repeat Node Based on Bidirectional Search
计算机科学, 2021, 48(11A): 77-80. https://doi.org/10.11896/jsjkx.201200193
[9] 陈继清, 谭成志, 莫荣现, 王志奎, 吴家华, 赵超阳.
基于人工势场的A*算法的移动机器人路径规划
Path Planning of Mobile Robot with A* Algorithm Based on Artificial Potential Field
计算机科学, 2021, 48(11): 327-333. https://doi.org/10.11896/jsjkx.200900170
[10] 赵晓薇, 朱小军, 韩周卿.
面向定位应用的无人机的悬停位置和飞行路径优化
Hover Location Selection and Flight Path Optimization for UAV for Localization Applications
计算机科学, 2021, 48(11): 345-355. https://doi.org/10.11896/jsjkx.201000105
[11] 王梓强, 胡晓光, 李晓筱, 杜卓群.
移动机器人全局路径规划算法综述
Overview of Global Path Planning Algorithms for Mobile Robots
计算机科学, 2021, 48(10): 19-29. https://doi.org/10.11896/jsjkx.200700114
[12] 杨德成, 李凤岐, 王祎, 王胜法, 殷慧殊.
智能3D打印路径规划算法
Intelligent 3D Printing Path Planning Algorithm
计算机科学, 2020, 47(8): 267-271. https://doi.org/10.11896/jsjkx.190700184
[13] 姜辰凯, 李智, 盘书宝, 王勇军.
基于改进Dijkstra算法的AGVs无碰撞路径规划
Collision-free Path Planning of AGVs Based on Improved Dijkstra Algorithm
计算机科学, 2020, 47(8): 272-277. https://doi.org/10.11896/jsjkx.190700138
[14] 曾伟良, 吴淼森, 孙为军, 谢胜利.
自动驾驶出租车调度系统研究综述
Comprehensive Review of Autonomous Taxi Dispatching Systems
计算机科学, 2020, 47(5): 181-189. https://doi.org/10.11896/jsjkx.190400031
[15] 王伟光, 尹健, 钱祥利, 周子航.
基于动态系统的多障碍实时规避算法
Realtime Multi-obstacle Avoidance Algorithm Based on Dynamic System
计算机科学, 2020, 47(11A): 111-115. https://doi.org/10.11896/jsjkx.200800068
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!