在“3060”双碳战略目标下, 随着风电产业的迅速发展, 预防性维护已然成为提高风力发电机运行可靠性的重要手段。关于复杂环境下风力发电机的运行管理尚存在对退化状态认识不充分、维修策略不可靠等问题。本文以期望成本最小化为目的, 考虑风力发电机关键部件退化过程与多阶段预防性维护策略的关系, 构建基于Markov状态转移的多阶段预防性维护成本模型。首先, 依据设备衰退规律, 利用Markov链的转移概率对退化状态、维护策略进行刻画。其次, 引入可靠度、故障率、役龄因子计算多阶段预防性维护时长和故障时长; 再进一步考虑天气条件对维护成本模型的影响, 构建风力发电机在整个维护周期内的期望成本模型。最后, 利用数值分析对模型进行求解。结果表明, 实施最小、更换维护策略的期望成本占据了总维护成本的80%以上, 相比之下, 采取预防性维护优化策略的期望成本则低于总维护成本的20%, 故该策略可为低风速地区的风电企业提供有效的决策参考, 提高风力发电机的运行可靠性。

The wind power industry is rapidly developing under the '3060' dual-carbon strategy. Preventive maintenance has become crucial in improving the operational reliability of wind turbines. However, operational management of wind turbines in complex environments still lacks sufficient understanding of the degradation state and reliable maintenance strategies. This paper examines the relationship between the degradation process of wind turbine key components and a multi-stage preventive maintenance strategy. The objective is to minimize expected costs. Based on Markov degraded state transfer, a multi-stage preventive maintenance cost model is constructed. This paper describes a model that utilizes the equipment decline law and Markov chain to portray the degraded state and maintenance strategy. It also introduces reliability, failure rate, and service age factors to calculate multi-stage preventive maintenance and failure hours. Additionally, the model considers the influence of weather conditions on the maintenance cost of wind turbines throughout the maintenance cycle. Numerical simulation is used to solve and analyze the model. The results indicate that the minimum and replacement maintenance strategy accounts for over 80% of the total maintenance cost, while the preventive maintenance optimization strategy accounts for less than 20%. Therefore, wind power enterprises in low wind speed areas can use this strategy as a practical reference for decision-making to enhance the operational reliability of wind turbines.