Noise Modeling for Power Line Communication Channel Using the LS-SVM and Wavelet Neural Networks
Zhang Hui1, Lu Wenbing1, Zhao Xiongwen1, Li Liang1, Liu Junyu2
1. Institute of Electrical & Electronic Engineering North China Electric Power University Beijing 102206 China; 2. State Grid information and Telecommunication Group Co. Ltd Beijing 100070 China
Abstract Power line communication (PLC) is an important communication way in smart grid. PLC channel noise is complex in such environment. It is essential to establish PLC channel noise model for in-depth study of the performance of low-voltage PLC in smart grid. This paper proposes two PLC channel noise models based on the least square support vector machine (LS-SVM) and wavelet neural network, respectively aiming at characterizing low-voltage PLC channel noise. To validate and compare their applicability to the time-variant PLC channels, noise measurements of low-voltage PLC channels in indoor and outdoor scenarios were carried out, the accuracy and efficiency of two models were studied based on large amount of measurement data. The results show that both models can simulate and adapt to the time-varying low-voltage PLC channels very well, while LS-SVM model has shorter simulation time and higher accuracy. Moreover, the proposed noise models are compared with traditional Markovian-Gaussian model. The results show that our proposed noise models have higher accuracy and lower complexity, especially the LS-SVM model is more appropriate to be applied as a noise generator instead of current Markovian-Gaussian model. The proposed models are helpful for investigating EMI on internal and external electromagnetic sources in the PLC and wireless.
Zhang Hui,Lu Wenbing,Zhao Xiongwen等. Noise Modeling for Power Line Communication Channel Using the LS-SVM and Wavelet Neural Networks[J]. Transactions of China Electrotechnical Society, 2018, 33(16): 3879-3888.
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2018, Vol. 33 
