基于Hilbert边际谱和SAE-DNN的局部放电模式识别方法

Pattern Recognition of Partial Discharge Based on Hilbert Marginal Spectrum and Sparse Autoencoder-Deep Neural Networks

提出了一种基于Hilbert边际谱和稀疏自编码器(SAE)—深度神经网络(DNN)的局部放电(PD)信号的模式识别方法。首先,以变分模态分解(VMD)对PD信号进行分解,对所得各分量进行Hilbert变换构建相应的Hilbert边际谱。其次,以PD信号的Hilbert边际谱为输入数据,利用SAE自动学习复杂数据的内在特征来提取简明的数据特征表达获得参数。再次,利用SAE的训练结果初始化DNN,再以大量训练样本进行分类器的训练。同时,为了加快SAE和DNN学习过程的收敛速度,以自适应步长的学习速率对网络进行调优,更新权值参数。最后,用训练好的DNN完成测试样本的PD类型的识别。此外,以基于BP神经网络和支持向量机的识别结果与文中结果进行比较。实验结果证明,所采用的识别方法具有更高的正确识别率。

A method based on Hilbert marginal spectrum and sparse autoencoder(SAE)-deep neural networks(DNN)is proposed to recognize partial discharge(PD)types.Firstly,PD signals are dealt with variational mode decomposition(VMD),and these obtained modes are used to construct corresponding Hilbert marginal spectrum by Hilbert transformation.Secondly,a Hilbert marginal spectrum of PD signal is taken as an input vector,and SAE can learn the inherent features and extract the succinct expressions from original data automatically.Thirdly,the results obtained by SAE are used to initialize DNN which is trained by a large number of samples.In the meanwhile,in order to speed up the convergence in the processes of learning for SAE and DNN,the network is optimized with the adaptive-step learning rate and updated with the weight parameters.Finally,DNN is trained well to identify the PD types of samples.Besides,compared with the results based on BP neural networks and support vector machines,the results based on SAE-DNN can achieve a higher accuracy.