Wind turbine generator state parameter abnormity identification method based on combination prediction
A state parameter and combined prediction technology, applied in neural learning methods, biological neural network models, etc., can solve problems such as difficulty in predicting whether an abnormality occurs in wind turbines, difficulty in identifying abnormal state parameters of wind turbines, and difficulty in identifying abnormality in wind turbines.
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[0200] Taking the No. 13 and No. 27 wind turbines of a domestic wind farm as an example, the SCADA data of different wind turbines are used to compare and verify the parameter anomaly identification methods. No. 13 wind turbine generator bearing B overheating fault occurred on May 30, 2012. In order to study the change of the status parameters of the unit, the monitoring data for a period of time before the fault occurrence is selected, and the selection is from March 1 to May 30, 2012 The daily SCADA data (about 90 days in total) are research data. On July 30, 2012, the generator bearing B overheating failure occurred in the No. 27 wind turbine. The SCADA data from May 16 to July 30, 2012 (about 73 days in total) were selected as the research data. According to the wind farm site setting, the upper limit of the generator bearing temperature is 95°C.
[0201] Image 6 It is the analysis result of the temperature parameter of the generator bearing B of the No. 1...
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