基于最小二乘支持向量机和小波神经网络的电力线通信信道噪声建模研究

doi:10.19595/j.cnki.1000-6753.tces.170961

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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.

Key words： Least square support vector machine (LS-SVM) wavelet neural network low-voltage power line communication (PLC) noise

Received: 10 July 2017 Published: 03 September 2018

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TN913.6

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	Zhang Hui
	Lu Wenbing
	Zhao Xiongwen
	Li Liang
	Liu Junyu

Cite this article:

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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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170961 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I16/3879

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