风电机组关键部件故障机理与状态评估方法

Wind turbine failures not only affect the benefits of business owners, but also threaten the safe operation of the power grid. How to reduce the losses from failures and avoid the major breakdowns have become major issues needed to be solved in wind farm operation, and the condition monitoring and assessment on wind turbines will be a very effective solution. Compared with the traditional electric equipment, wind turbines have apparent differences in mechanics and materials, working environment, and electrical topology. There are new features and problems with wind turbines, particularly with the wind turbine blades, gearboxes, and electrical control system. The research carried out in this project include: research on the electrical control system fault mode and condition characterization of wind turbines, research on the defect development process and condition characterization of the blades, research on the gear box fault mode and condition characterization, research on the effect that power grid faults have on the key equipment for wind turbines health, research on multi-parameters based evaluation of wind turbines' local and overall condition. Then the assessment system of the wind turbines will be developed. The research will solve the key problems in condition monitoring of wind turbines, and promote the transition from regular maintenance and repair to condition based maintenance of wind turbines

风电机组故障不仅影响着运营业主的经验效益，更是严重威胁着电网运行安全。如何有效降低故障带来的损失，避免重大故障发生已成为风电场运行中必须解决的重大问题，对风电机组实施状态监测与评估是非常有效的应对措施。与传统动电力设备部件相比，风电机组无论是在机械结构和材料、工作环境，还是电气拓扑方面都与传统电力设备有着明显的差异，有新特点和新问题，特别是风电机组叶片、齿轮箱、电气控制系统等部件。本项目将开展风电机组的电气控制系统故障模式与状态表征方法研究，叶片缺陷的发展过程与状态表征方法研究，齿轮箱故障模式与状态表征方法研究，电网故障对风电机组关键设备健康状态影响研究，研究了基于多状态参量实现风电机组部件和整体状态的评估方法，建立了风电机组状态评估体系。项目研究成果将为解决风电机组的状态监测中的关键科学问题，为推动风电机组从定期维修和事后维修到状态检修的转变奠定基础

风电机组的状态监测与评估可有效提升风电机组运行的稳定与可靠性，本项目在风电机组的故障机理、监测方法、状态评估系统等方面开展了研究。项目建立了适用于风电机组状态评估和故障诊断的全景信息模型，研究了叶片缺陷的发展过程与状态监测方法，提出了基于气动噪声信号监测的叶片缺陷状态监测方法与基于激光超声波成像的风电机组叶片诊断方法；研究了风力发电机齿轮箱结构机理及故障类型特性，建立了齿轮箱典型故障信号特征库，提出了基于人工蜂群算法的选择性神经网络集成算法和基于改进随机森林的智能齿轮诊断模型的风机齿轮箱故障诊断方法；分析了电网故障对风电机组关键设备健康状态影响，搭建了双馈风电机组的仿真模型，开展了电网故障过程中风机暂态运行特性的仿真分析；提出了风电机组健康状态评估与状态预测方法，并研制了风机状态监测与故障诊断装置，结合现场测试数据验证了方法的有效性。本项目的研究成果解决了风电机组的状态监测与评估中的关键科学问题，为推动风电机组从定期维修和事后维修到状态检修的转变提供了理论基础，对风电机组的安全稳定运行具有重大意义。