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- W2349638246 abstract "It is hard to extract effective feature quantities from the stator currents when some early faults occur in the wind power generator.A novel algorithm that combining the wavelet transform improved by single-band reconstruction and BP network is proposed to solve this problem.By using the wavelet transform improved by single-band reconstruction to the stator currents of wind power generator,the frequency aliasing in the traditional wavelet transform is eliminated.Then,the characteristic field and features from each sub-band signals that are generated from the wavelet transform are selected as the input of BP neural network.On this basis,by using BP neural network which has the input-output nonlinear mapping ability,the diagnosis and location of the fault are completed.The simulated results show that the method can accurately diagnose the faults of wind turbine generator." @default.
- W2349638246 created "2016-06-24" @default.
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- W2349638246 date "2012-01-01" @default.
- W2349638246 modified "2023-09-23" @default.
- W2349638246 title "Fault Diagnosis of Wind Power Generator Based on Improved Wavelet and BP NN" @default.
- W2349638246 hasPublicationYear "2012" @default.
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