低压电力线信道特性与噪声模型的研究
发布时间：2019-03-02 17:39
【摘要】：低压电力线通信(Power Line Communication,PLC)技术具有无需重新布线等独特的优点,被广泛应用于各个领域。噪声是影响PLC通信系统可靠性的主要因素之一,它会恶化通信质量,甚至会造成整个通信过程的中断。因此,研究电力线通信系统内噪声的高精度模型对提高该系统的抗噪能力意义深远。本文主要研究了低压PLC信道中背景噪声的高精度建模问题。相关研究内容与成果如下:1、着重介绍了电力线信道的噪声特性。在MATLAB上分别对有色背景噪声及窄带噪声进行仿真,得到其时域波形及功率谱密度(Power Spectrum Density,PSD),作为本论文后续噪声建模问题研究的源数据使用。2、在对有色背景噪声进行小波峰式马尔科夫链建模时,研究了不同小波基函数对建模效果的影响。通过计算建模前后噪声功率谱密度的均方根误差确定了具有最高建模精度的小波基函数。3、给出一种基于小波神经网络的新型背景噪声模型。对有色背景噪声及窄带噪声分别进行小波神经网络建模,对比所建模型输出噪声与测试噪声的时域波形及PSD,计算两者功率谱密度的均方根误差,并将该模型的建模效果与传统的小波峰式马尔科夫链模型相对比。4、针对小波神经网络具有隐层节点个数难以确定的缺点,给出一种基于LS-SVM的新型背景噪声模型。对有色背景噪声及窄带噪声分别开展基于LS-SVM模型的建模研究,对比所建模型输出噪声与测试噪声的时域波形及功率谱密度,计算两者功率谱密度的均方根误差,并将该模型的建模效果与小波峰式马尔科夫链模型进行对比,验证LS-SVM模型的优劣。研究结果表明,Daubecies、Biorthogonal和Haar小波基函数中,使用Daubecies小波基函数的小波峰式马尔科夫链的建模精度最高;小波神经网络和LS-SVM模型输出噪声与测试噪声的时域波形及功率谱密度均有着较一致的变化趋势;两种模型的建模误差均小于小波峰式马尔科夫链模型。综上所述,Daubecies小波可选为有色背景噪声进行小波峰式马尔科夫链建模的最佳小波基函数;小波神经网络和LS-SVM模型对背景噪声的建模均是有效的,它们的建模精度均高于传统的小波马尔科夫链。
[Abstract]:Low voltage power line communication (Power Line Communication,PLC (low voltage power line communication) technology is widely used in various fields because of its unique advantages such as no re-wiring and so on. Noise is one of the main factors affecting the reliability of PLC communication system. It will worsen the communication quality and even cause the interruption of the whole communication process. Therefore, the study of the high-precision model of internal noise in power line communication system is of great significance to improve the anti-noise capability of the system. In this paper, the high-precision modeling of background noise in low-voltage PLC channel is studied. The related research contents and achievements are as follows: 1. The noise characteristics of power line channel are emphatically introduced. The colored background noise and narrow band noise are simulated on MATLAB, and their time domain waveforms and power spectral density (Power Spectrum Density,PSD) are obtained, which can be used as the source data for further research of noise modeling in this paper. In the modeling of colored background noise by wavelet peak Markov chain, the influence of different wavelet basis functions on the modeling effect is studied. The wavelet basis function with the highest modeling accuracy is determined by calculating the root mean square error of noise power spectral density before and after modeling. 3. A new background noise model based on wavelet neural network is presented. The colored background noise and narrow band noise are modeled by wavelet neural network, and the RMS error of power spectral density between the output noise and the test noise in time domain and the PSD, calculation are compared. The modeling effect of the model is compared with the traditional Markov chain model. 4. Aiming at the disadvantage that the number of hidden layer nodes in the wavelet neural network is difficult to determine, a new background noise model based on LS-SVM is proposed. The modeling of colored background noise and narrow band noise based on LS-SVM model is studied. The time domain waveform and power spectral density of output noise and test noise are compared, and the root mean square error of power spectral density of the two models is calculated. The modeling effect of this model is compared with that of wavelet peak Markov chain model, and the advantages and disadvantages of the LS-SVM model are verified. The results show that in Daubecies,Biorthogonal and Haar wavelet basis functions, the modeling accuracy of wavelet peak Markov chain using Daubecies wavelet basis function is the highest. The output noise of wavelet neural network and LS-SVM model is consistent with the time domain waveform and power spectral density of test noise, and the modeling error of the two models is smaller than that of wavelet peak Markov chain model. To sum up, Daubecies wavelet can be selected as the best wavelet basis function for wavelet peak Markov chain modeling with colored background noise. Both wavelet neural network and LS-SVM model are effective in modeling background noise, and their modeling accuracy is higher than that of traditional wavelet Markov chain.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN913.6
[Abstract]:Low voltage power line communication (Power Line Communication,PLC (low voltage power line communication) technology is widely used in various fields because of its unique advantages such as no re-wiring and so on. Noise is one of the main factors affecting the reliability of PLC communication system. It will worsen the communication quality and even cause the interruption of the whole communication process. Therefore, the study of the high-precision model of internal noise in power line communication system is of great significance to improve the anti-noise capability of the system. In this paper, the high-precision modeling of background noise in low-voltage PLC channel is studied. The related research contents and achievements are as follows: 1. The noise characteristics of power line channel are emphatically introduced. The colored background noise and narrow band noise are simulated on MATLAB, and their time domain waveforms and power spectral density (Power Spectrum Density,PSD) are obtained, which can be used as the source data for further research of noise modeling in this paper. In the modeling of colored background noise by wavelet peak Markov chain, the influence of different wavelet basis functions on the modeling effect is studied. The wavelet basis function with the highest modeling accuracy is determined by calculating the root mean square error of noise power spectral density before and after modeling. 3. A new background noise model based on wavelet neural network is presented. The colored background noise and narrow band noise are modeled by wavelet neural network, and the RMS error of power spectral density between the output noise and the test noise in time domain and the PSD, calculation are compared. The modeling effect of the model is compared with the traditional Markov chain model. 4. Aiming at the disadvantage that the number of hidden layer nodes in the wavelet neural network is difficult to determine, a new background noise model based on LS-SVM is proposed. The modeling of colored background noise and narrow band noise based on LS-SVM model is studied. The time domain waveform and power spectral density of output noise and test noise are compared, and the root mean square error of power spectral density of the two models is calculated. The modeling effect of this model is compared with that of wavelet peak Markov chain model, and the advantages and disadvantages of the LS-SVM model are verified. The results show that in Daubecies,Biorthogonal and Haar wavelet basis functions, the modeling accuracy of wavelet peak Markov chain using Daubecies wavelet basis function is the highest. The output noise of wavelet neural network and LS-SVM model is consistent with the time domain waveform and power spectral density of test noise, and the modeling error of the two models is smaller than that of wavelet peak Markov chain model. To sum up, Daubecies wavelet can be selected as the best wavelet basis function for wavelet peak Markov chain modeling with colored background noise. Both wavelet neural network and LS-SVM model are effective in modeling background noise, and their modeling accuracy is higher than that of traditional wavelet Markov chain.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN913.6
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相关期刊论文 前10条
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2 姚海燕;张静;留毅;潘姝;徐贝;李题印;周念成;;基于多尺度小波判据和时频特征关联的电缆早期故障检测和识别方法[J];电力系统保护与控制;2015年09期
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