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中文摘要：
由非线性电力电子装置组成的风力机变频器一旦发生故障,其故障特征信息不容易被提取和识别。为此,提出了一种基于小波包分析和Elman神经网络的电力电子装置故障诊断的方法,先运用小波包分析法提取电力电子装置电路在不同故障状态下电压及电流信号的特征信息,然后对数据进行归一化处理并作为Elman神经网络的输入,由具有智能学习功能的神经元故障分类器完成故障识别和定位。以典型的风力机交—直—交变频器为例,在Matlab软件下建立电路模型对一次侧故障进行仿真实验,结果表明采用该方法可以快速、准确地完成故障诊断。
英文摘要：
It＇s hard to extract and identify effective fault feature,when the fault of wind turbine frequency converter composed of nonlinear power electronic device happened.A method based on wavelet packet analyzing and Elman neural network was proposed to fault diagnose of power electronic device.Firstly,the signal feature of different states about voltage and current of power electronic device was extracted by using the wavelet packet analysis.Then the data was normalized and being inportted the Elman neural network,the neural network classifier will identify and diagnose different faults.Taking the typical wind turbine AC-DC-AC frequency converter as an example,completed the circuit model under Matlab and put up emulation experiment.The result of emulation shows that the method can deal with the fault diagnosis rapidly and accurately.
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