High proportion power electronic noise modeling based on DCNN for power line communications

Bo Li; Zhan Shi; Zhihua Yang; Xiuzhu Wang

doi:10.1117/12.3012210

21 December 2023 High proportion power electronic noise modeling based on DCNN for power line communications

Bo Li, Zhan Shi, Zhihua Yang, Xiuzhu Wang

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297004 (2023) https://doi.org/10.1117/12.3012210
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

To improve the stability of high-proportion power electronic systems for power line communications and reducethenoise effect of power electronic equipment operation, this paper analyzes the principle and architecture of the deepconvolutional neural network (DCNN) algorithm. The noise modeling method based on the prediction parameters ofDCNN is proposed to reduce the reconstruction error and improve the noise immunity. Simulation results verifiedthat the proposed algorithm achieves superior performance in peak signal-to-noise ratio (PSNR) and reconstruction error.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bo Li, Zhan Shi, Zhihua Yang, and Xiuzhu Wang "High proportion power electronic noise modeling based on DCNN for power line communications", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297004 (21 December 2023); https://doi.org/10.1117/12.3012210

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KEYWORDS

Deep convolutional neural networks

Photonic integrated circuits

Signal to noise ratio

Convolution

Modeling

Background noise

Reconstruction algorithms

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