Research on Modeling of Low-Voltage Power Line Background Noise by Wavelet Neural Networks

doi:10.11930/j.issn.1004-9649.201606111

Abstract

Abstract: In order to improve anti-interference ability of power line communications, a new modeling method for colored background noise and narrowband noise based on wavelet neural network is proposed. Firstly, the background noise is modeled by wavelet neural network. The output noise waveforms and power spectrum densities (PSD) obtained from model are compared with test noise by calculating root mean square error(RMSE). Moreover, the background noise is also modeled by traditional wavelet packet transform and peak typed Markov chain. RMSEs of PSDs before and after modeling are also calculated. Simulation results show that both output noise waveforms and PSD obtained by proposed model have good agreements with test noise. The RMSE is smaller than the value generated using wavelet packet transform and peak typed Markov chain. Therefore, the proposed wavelet neural network model is effective in modeling background noise, especially for the wideband colored background noise.

Key words: colored background noise, narrowband background noise, wavelet neural network, wavelet packet transform and peak typed Markov chain

CLC Number:

TM73
TN913

SUO Chaonan, ZHAO Xiongwen, ZHANG Hui, LU Wenbing. Research on Modeling of Low-Voltage Power Line Background Noise by Wavelet Neural Networks[J]. Electric Power, 2017, 50(9): 89-94.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201606111

https://www.electricpower.com.cn/EN/Y2017/V50/I9/89

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