【摘要】 风电清洁又安全，然而，风力发电机存在维护成本高、难度大的特点。在将风力发电机作为多状态系统进行维护决策优化时，其各个状态的性能水平、状态转移概率和维护成本等很难精准得知，因而模型中会产生认知不确定性。以特定型号风力发电机的实际运行情况为背景，利用其运行过程中可能出现的多个性能水平进行多状态退化建模，提出一种基于模糊马尔科夫决策过程的风力发电机维护决策优化方法，再用三角模糊数量化模型中的状态转移概率、设备单位时间收益等参数的认知不确定性，最终实现求解。该方法旨在考虑模型认知不确定性下优化合理的维护决策，在工程中更好地实现兆瓦级风力发电机系统的收益最大化。更多还原

【Abstract】 Wind energy is safe and clean,but,the maintenance of wind turbines which are main device to produce wind energy is a challenge and expensive.Exact values of the performance rates(levels),state transition probability and maintenance cost is unable to get when the wind turbine is modeled as an multi-state system(MSS).Therefore,a maintenance optimization method is proposed for the wind turbine under this epistemic uncertainty.In the proposed method,a selective maintenance model is formulated for the wind turbine generator system,which can be viewed as a multi-state system.The Markov decision process is proposed to resolve the selective maintenance problem.By combining the Markov decision process with the fuzzy theory,the reward and the state distribution of the system are represented as triangular fuzzy numbers to quantify the epistemic uncertainty.By the proposed method,the maintenance decision of the wind turbine generator system can be well-optimized.更多还原