基于退化状态空间划分的风电机组视情维修决策
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(1. 太原科技大学工业与系统工程研究所,太原030024;2. 中北大学大数据学院,太原030051;3. 山西人文社科重点研究基地装备制造业创新发展研究中心,太原030024)

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E-mail: zengjianchao@263.net.

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TN165.3

基金项目:

国家自然科学基金项目(71701140);山西省青年基金项目(201601D021082, 201601D021065);山西省高等学校科技创新项目(201802091);太原科技大学校博士启动基金项目(20152021, 20162021).


Optimal decision of condition-based maintenance of wind turbines based on deterioration state-space partition
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(1. Division of Industrial and System Engineering,Taiyuan University of Science & Technology,Taiyuan 030024,China;2. Division of Big Data and Visual Computing,North University of China,Taiyuan 030051,China;3. Research Centre for Innovation and Development of Equipment Manufacturing Industry,Key Research Bases for Humanities and Social Sciences in Shanxi,Taiyuan 030024,China)

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    摘要:

    风场的运维成本约占其收入的三分之一之多,风电机组的最优维修问题一直是风电系统降低运维成本的主要途径.针对同一风场多台风力机组成的系统,制定基于状态检测的视情机会维修策略,提出基于退化状态空间划分的多设备系统状态维修决策建模方法,在此基础上建立维修成本最小的解析模型,以决策风力机最优的状态检测周期和维修阈值.实验结果表明,基于状态监测的风力机视情维修机会方案可以很好地节约系统运维成本.

    Abstract:

    The operation and maintenance cost of wind farms accounts for about 1/3 of its revenue. The optimal maintenance of wind turbines is always the main way to reduce operation and maintenance cost of wind farms. Focusing on a system composed of multiple identical wind turbines, this paper presents a condition-based opportunity maintenance strategy for it. A deterioration state space partition method is proposed to analyze and model the opportunity maintenance strategy for the multi-unit system. Then, an analytic model is established based on the presented modeling method to determine the optimal inspection period of the system, and opportunistic and preventive maintenance thresholds of each unit jointly for minimizing the long-term cost rate. The experimental result verifies the performance of the modeling method. It also shows that the joint optimal maintenance decision can achieve significant performance for the wind turbine.

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张晓红,曾建潮,石冠男,等.基于退化状态空间划分的风电机组视情维修决策[J].控制与决策,2019,34(9):1909-1916

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  • 在线发布日期: 2019-09-06
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