Review article of American Journal of Scientific Research and Essays
Wind power cabin high temperature hot spot remote intelligent monitoring and early warning system
College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
With the increasing energy crisis, the development and utilization of wind energy, which is a substitute for traditional energy sources and has renewable and cleanliness, has gradually attracted the attention of people all over the world. While the wind power industry is growing rapidly and the unit capacity of the unit is gradually increasing, the operational safety of wind power equipment is receiving more and more attention. If the wind turbine component fails, the small one will result in downtime maintenance, and the other will cause the machine to be destroyed. Because the wind farm is built in remote areas or offshore areas far away from the city, the traffic is inconvenient, and the wind turbines are at high altitude, so it is very difficult to maintain the unit. Once a fault or fire occurs, it is difficult to rescue in time. Let it develop its accident. In view of the high cost of detection, the failure of detecting faults, and the high proportion of missed faults, the on-line monitoring of the operating status of wind power equipment, the real-time diagnosis of early warning faults, and the maintenance of equipment are also important. The online monitoring system is applied to the health monitoring of wind power equipment, which not only can alert the equipment to potential failures, avoid the occurrence of major accidents, but also can determine the fault type and severity of the equipment based on the integration of existing analytical methods. Save on operational maintenance and repair costs. At a deeper level, mining and analyzing the monitoring data can also complete the estimation of the equipment’s continuous running time before the major accident, promptly remind the staff and provide reasonable rescue plans and solutions.
Keywords: wind turbine; high temperature; Intelligent monitoring; Big Data; Early warning
How to cite this article:
Xiaoxiao Chen. Wind power cabin high temperature hot spot remote intelligent monitoring and early warning system. American Journal of Scientific Research and Essays, 2019 4:22. DOI:10.28933/ajsre-2019-06-0706
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