Modelling and comparison for low-voltage broadband power line noise using LS-SVM and wavelet neural networks

作者：Zhao, Xiongwen; Zhang, Hui^*; Li, Liang; Lu, Wenbing; Ding, Yi; Liu, Junyu

来源：IET Communications, 2019, 13(2): 171-178.

DOI：10.1049/iet-com.2018.5282

• least squares approximations
• learning (artificial intelligence)
• telecommunication channels
• support vector machines
• carrier transmission on power lines
• telecommunication computing
• autoregressive processes
• wavelet neural nets
• time-varying channels
• classical machine learning algorithms
• time-variant PLC channels
• noise measurements
• low-voltage PLC channels
• noise models
• time-variant low-voltage broadband PLC channels
• LS-SVM model
• shorter simulation time
• noise generator

This study is to construct the autoregressive models for the low-voltage broadband power line communication (PLC) channel noise by two machine learning algorithms, namely the least square support vector machine (LS-SVM) and wavelet neural networks. The main work is to compare the two classical machine learning algorithms and also compare them with the traditional Markovian-Gaussian method. To verify their availability and ability to adapt to the time-variant PLC channels, noise measurements for low-voltage PLC channels in indoor and outdoor scenarios are carried out. The accuracy and efficiency of the two models are studied and compared based on a large amount of measurement data. The results show that both of the noise models can simulate and adapt to the time-variant low-voltage broadband PLC channels very well. The LS-SVM model is found to have shorter simulation time and higher accuracy. Moreover, the proposed noise models are also compared with the traditional Markovian-Gaussian model. The results show that both the proposed noise models exhibit higher accuracy and lower complexity, especially that the LS-SVM is more appropriate to be applied as a noise generator in PLC link and network level simulations instead of the current Markovian-Gaussian model.

• 出版日期2019-1-18
• 单位华北电力大学; 华北电力大学（保定）