期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：237
期号：3
页码：032109
DOI：10.1088/1755-1315/237/3/032109
出版社：IOP Publishing
摘要：As one of the most widely applied clean energy resources, wind power generation is helpful for energy structure adjustment and sustainable development, whereas blade-icing failure affects the stability and benefits of wind farm. In consideration of efficiency improvement and safety hazard control, this paper proposed a failure detection model based on long short-term memory network. With the combination of turbine operation mechanism and data-driven method, the early warning of blade icing was implemented, which made it possible to take timely measures such as turning on the de-icing system at early moments of icing issues. The operational data collected by SCADA system were used as input for model, and progressive trend as well as hidden messages were learned from historical correlation at both temporal and characteristic scales. Consequently, the model achieved the fault detection and performed well on test dataset.
