海上风电机组智能故障预警系统研究

  目的]  随着海上风电机组装机容量的飞速发展，业主对海上风电机组的安全运行越来越重视，对风机设备可靠性的要求越来越高。传统的设备故障事后处理模式不仅不能保证发电设备运行的可靠性，而且海上风电运行维护的可达性差，被动的故障后维修无形中增加了巨大的电量损失，已完全不能满足海上风电的要求。设备故障早期智能预警系统可以提前预知设备存在的问题，把设备隐患消除在萌芽状态之内，真正做到“防患于未然”。  方法]  通过对海上风电机组关键部件的数据采集，结合历史数据提取故障特征，利用神经网络等大数据算法，实现发电机温度异常、发电机轴承异常、齿轮箱散热异常、齿形带断裂警告等设备故障的提前预判。  结果]  根据对设备早期故障的提前预判，可以综合考虑海上风电的气象、台风、海况、海事等维护特点，有计划地执行积极的预防性维护策略，能够有效地避免大部件故障的发生或风机整机失效情况的发生。  结论]  研究成果可提高海上风电机组的可靠性和风电场整体发电效益。

Research on Intelligent Fault Warning System of Offshore Wind Turbines

  Introduction]  With the rapid development of offshore wind turbine installed capacity, the owner attaches more and more importance to safe operation of offshore wind turbines, and imposes more stringent requirements for reliability. The traditional after-fault trouble-shooting pattern cannot ensure reliability of offshore wind power equipment. Moreover, as the accessibility of the offshore wind power equipment is unfavorable, the passive trouble-shooting pattern leads to huge loss of outgoing power, which fails to meet the latest requirements of the modern offshore wind farm. The intelligent fault warning system can predict the abnormal conditions of equipment and eliminate the hidden danger at its very beginning stage, preventing it from further deterioration.  Method]  The forecast of critical faults, such as generator temperature abnormal, generator bearing abnormal and gear box heat dissipation abnormal and cog belt rupture, can be achieved in advance, by collecting the data of wind turbine unit critical components, summing up the fault characteristics from historical data and employing the big data algorithm including neural network.  Result]  In accordance with early warning of equipment faults, active and preventive maintenance strategy can be practiced in a planned manner, in combination with the OWF maintenance characteristics of meteorology, typhoon, oceanic and maritime conditions. Thus, large component faults and wind turbine unit failures can be effectively prevented.  Conclusion]  The research results could enhance the wind turbine unit reliability and ensure the overall gains of the offshore wind farm.