摘要
随着风力发电行业的快速发展,风力发电机组装机数量迅猛增加。然而恶劣的工作环境导致风力发电机组故障率较高。通过故障诊断技术及时发现设备存在的故障,进而确保发电机组安全可靠运行,是降低事故的有效途径。提出一种风力发电机组早期故障诊断的方法,通过对风力发电设备运行状态进行实时监测并进行趋势预测,及时发现运行过程中存在的故障隐患,并利用信号处理方法 EEMD对故障信号进行分析处理,提取故障特征信息,进而诊断故障发生的原因和部位等情况,最后综合评价决策釆取适当的应对措施,并通过实验验证了该方法的有效性。
With the rapid development of the wind power industry ,the number of wind turbines is also rapidly increasing .Howev-er,poor working conditions led to a high failure rate of wind turbines by fault diagnosis techniques to detect the presence of equip -ment failure ,thereby to ensure wind turbines run safely and securely ,which is an effective way to reduce accidents .It presents a wind turbine early fault diagnosis method .Through real-time monitoring of running status of the wind turbine and trend forecas-ting,hidden faults are timely detected of the presence of faults in the process of operation .And using Ensemble Empirical Mode Decomposition （ EEMD） fault signal analysis and processing method to extract fault feature information .Furthermore diagnosed the cause and location of the fault ,finally the appropriate measures are taken .Experiments show the effectiveness of this method .
出处
《现代制造工程》
CSCD
北大核心
2015年第3期126-131,共6页
Modern Manufacturing Engineering
基金
国家自然科学基金项目(51275052)
北京市自然科学基金资助重点项目(KZ201311232036
3131002
KZ201211232039)
北京市重点实验室开放课题项目(KF20121123202)
关键词
早期故障诊断
BP神经网络趋势预测
总体经验模式分解
early fault diagnosis
BP network trend forecasting
Ensemble Empirical Mode Decomposition （EEMD）
