电力系统自动化2019，Vol.43Issue(1)：87-94,8.DOI:10.7500/AEPS20180422004
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
摘要
Abstract
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.关键词
局部放电/模式识别/Hilbert边际谱/稀疏自编码器/深度神经网络Key words
partial discharge/pattern recognition/Hilbert marginal spectrum/sparse autoencoder (SAE)/deep neural network (DNN)引用本文复制引用
高佳程,朱永利,郑艳艳,张科,刘帅..基于Hilbert边际谱和SAE-DNN的局部放电模式识别方法[J].电力系统自动化,2019,43(1):87-94,8.基金项目
国家自然科学基金资助项目(51677072) （51677072）
中央高校基本科研业务费专项资金资助项目(2017XS118) This work is supported by National Nature Science Foundation of China (No. 51677072) and Fundamental Research Funds for the Central Universities (No. 2017XS1l8). （2017XS118）
