论文标题
电力电子转换器的故障诊断基于深馈电网络和小波压缩
Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression
论文作者
论文摘要
本文提出了基于深馈电网络和小波压缩的电力电子转换器的故障诊断方法。小波压缩后的瞬态历史数据用于实现故障诊断分类器的训练。首先，执行在各种故障状态下运行的电压或电流数据的相关分析以删除冗余特征和采样点。其次，小波变换用于删除特征的冗余数据，然后训练样本数据被极大地压缩。深馈网络由功能的低频组成部分训练，而训练速度则大大加速。故障诊断分类器的平均准确性可以达到97％以上。最后，测试了故障诊断分类器，并通过多组瞬态数据确定最终诊断结果，通过这些数据，诊断结果的可靠性得到了提高。实验结果证明了分类器具有强大的概括能力，并且可以准确地定位IGBT中的开路断层。
A fault diagnosis method for power electronics converters based on deep feedforward network and wavelet compression is proposed in this paper. The transient historical data after wavelet compression are used to realize the training of fault diagnosis classifier. Firstly, the correlation analysis of the voltage or current data running in various fault states is performed to remove the redundant features and the sampling point. Secondly, the wavelet transform is used to remove the redundant data of the features, and then the training sample data is greatly compressed. The deep feedforward network is trained by the low frequency component of the features, while the training speed is greatly accelerated. The average accuracy of fault diagnosis classifier can reach over 97%. Finally, the fault diagnosis classifier is tested, and final diagnosis result is determined by multiple-groups transient data, by which the reliability of diagnosis results is improved. The experimental result proves that the classifier has strong generalization ability and can accurately locate the open-circuit faults in IGBTs.
