Fault early warning method for wind power generator
A wind turbine and fault early warning technology, which is applied to wind turbines, wind turbine monitoring, engines, etc., can solve problems such as difficult model maintenance, low accuracy, and long time consumption, and achieve fault early warning and predictive stability High, high fault tolerance effect
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Embodiment 1
[0041] figure 1 This is a flowchart of a wind turbine fault early warning method provided in the first embodiment of the present invention, which specifically includes the following steps:
[0042] Step S101: Extract observation parameters;
[0043] The observation parameters are the observation data of multiple observation points in different time periods, which are extracted and constructed by the SCADA system to form an initial observation parameter matrix. Each column vector (called column sample) in the initial observation parameter matrix is collected at a certain time The values of parameters at different observation points of the wind turbine; each row vector (called a row sample) in the initial observation parameter matrix is the value of the parameters collected at different times for the same observation point of the wind turbine.
[0044] Step S102: Observation parameter cluster analysis, the construction of the state matrix can be classified as a clustering proble...
Embodiment 2
[0052] figure 2 It is a flowchart of a wind turbine fault early warning method provided in the second embodiment of the present invention. The second embodiment of the present invention specifically describes the data processing after the observation parameters are extracted based on the first embodiment.
[0053] Further, as figure 2 As shown, after the observation parameters are extracted in step S201, the operations of rough set attribute reduction in step S202 and observation parameter preprocessing in step S203 can also be performed. Step S202 rough set attribute reduction and step S203 observation parameter preprocessing are in the actual sense of sorting out historical operating data, reducing the scale of data processing, and improving the accuracy of fault early warning. After processing the historical operating data, step S204 observes parameter clustering analysis, step S205 "centroid" extraction construction state matrix and step S206 similarity modeling state estim...
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