电力通信系统中脉冲噪声处理方法研究

发布时间：2018-06-18 00:44

本文选题：电力通信 + 正交频分复用　；参考：《电子科技大学》2016年硕士论文

【摘要】：随着电网智能化的发展以及电气设备的普及,脉冲噪声对电力通信系统的影响变得日益严重。电力通信中脉冲噪声的研究越来越深入,但对脉冲噪声的抑制尚未形成有效处理机制。本文以IEEE 802.22无线区域网(WRAN)与IEEE 1901.2两种具有代表性的电力通信标准为背景,对脉冲噪声处理方法展开研究。IEEE 802.22与IEEE 1901.2物理层均采用正交频分复用(OFDM)作为调制方式,因此本文将对基于OFDM技术的电力数字通信系统展开研究,深入分析脉冲噪声对系统的影响以及抑制方法。现将论文完成的工作总结如下:(1)研究了电力线通信和WRAN通信的脉冲噪声特点,深入研究了脉冲噪声的三种统计模型:高斯模型Bernoulli-Gaussian、非高斯模型Middleton A类和基于电力线工频的KATA模型。对模型的数学表达和参数特点进行了详细地解析,仿真了噪声模型的时域序列和统计分布,并将噪声模型移植到仿真平台的信道模块中。(2)针对WRAN通信中时域非线性处理带来的波形失真问题,提出了Blanking非线性联合硬判决反馈补偿的脉冲噪声抑制算法。首先,介绍了Blanking和Clipping两种传统的非线性处理的脉冲噪声抑制方法,详细推导和修正了在Bernoulli-Gaussian模型下这两种抑制方法的SNR增益,得到了最优门限。根据OFDM的技术特点与Blanking处理的理论分析,提出了时频联合抑制算法。该算法最大特点是能抑制不同功率的脉冲噪声,同时还能补偿Blanking处理带来的波形失真,大幅提高了系统的抗干扰性能。(3)研究了PLC系统中基于交织技术的噪声处理方法。在IEEE 1901.2的基础上,利用FFT并行数据处理的特点,进行了两个方面的系统改进。首先,在接收信号解调前进行噪声预处理,引入限幅算法,这里主要是防止脉冲噪声随着FFT和块交织将噪声扩散到系统中,减少脉冲噪声的影响。其次,在IEEE 1901.2标准下的频域交织的基础上再加入时域交织算法,形成双重交织,能充分的将脉冲噪声白化,从而实现性能优化。
[Abstract]:With the development of intelligent power grid and the popularization of electrical equipment, the impact of impulse noise on power communication system becomes more and more serious. The research of impulse noise in power communication is more and more in-depth, but the suppression of pulse noise has not yet formed an effective processing mechanism. In this paper, based on the two typical power communication standards of IEEE 802.22 wireless area network (WRAN) and IEEE 1901.2, the pulse noise processing methods are studied. Both IEEE 802.22 and IEEE 1901.2 physical layer adopt orthogonal frequency division multiplexing (OFDM) as modulation mode. Therefore, in this paper, the power digital communication system based on OFDM technology will be studied, and the influence of impulse noise on the system and the suppression method will be analyzed. In this paper, the following work is summarized as follows: 1) the characteristics of impulse noise in power line communication and Wran communication are studied, and three statistical models of impulse noise are studied in depth: Bernoulli-Gaussianmodel Gao Si model, non-Gao Si model Middleton A model and Kata model based on power line power frequency. The mathematical expression and parameter characteristics of the model are analyzed in detail, and the time series and statistical distribution of the noise model are simulated. The noise model is transplanted to the channel module of the simulation platform. Aiming at the waveform distortion caused by nonlinear processing in time domain in Wran communication, a Blanking nonlinear combined hard decision feedback compensation algorithm for impulse noise suppression is proposed. Firstly, two traditional nonlinear methods for impulse noise suppression are introduced, such as blanking and clipping. The SNR gain of these two methods is deduced and modified in detail under Bernoulli-Gaussian model, and the optimal threshold is obtained. According to the technical characteristics of OFDM and the theoretical analysis of Blanking processing, a joint time-frequency suppression algorithm is proposed. The main characteristic of this algorithm is that it can suppress the pulse noise of different power and compensate the waveform distortion caused by Blanking processing. The anti-jamming performance of the system is greatly improved.) the noise processing method based on interleaving technology in PLC system is studied. On the basis of IEEE 1901.2, two aspects of system improvement are carried out by using the characteristics of parallel data processing. Firstly, noise preprocessing is performed before demodulation of received signals, and an amplitude limiting algorithm is introduced. The main purpose is to prevent impulse noise from spreading into the system with FFT and block interleaving, so as to reduce the influence of pulse noise. Secondly, on the basis of frequency domain interleaving in IEEE 1901.2 standard, the interleaving algorithm in time domain is added to form double interleaving, which can fully whiten the pulse noise and realize the performance optimization.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM73;TN911.4

【参考文献】