An accurate detection method for turbine icing issues using LSTM network. Issue 3 (February 2019)

• Record Type: Journal Article
• Authors: Li, Minglu; He, Xin
• Is Part Of: IOP conference series. Volume 237:Issue 3(2019)
• Publication Date: 2019-02
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/237/3/032109
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: 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.