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
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摘要
电力线通信是智能电网中的一种重要通信方式,电网中噪声干扰复杂,建立电力线通信信道噪声模型对于深入研究智能电网中低压电力线通信性能至关重要。针对低压电力线通信信道噪声特性,分别提出基于最小二乘支持向量机(LS-SVM)模型和小波神经网络模型在电力线信道噪声中的应用。为了验证并比较LS-SVM和小波神经网络模型对时变的低压电力线信道噪声建模的有效性,在室内和室外环境下对低压电力线通信信道的噪声进行测量,基于大量的测量数据,研究两个模型的准确度和效率。结果表明,两个噪声模型能够很好地仿真和适应时变的低压电力线通信信道,LS-SVM模型有更高的精度和更短的仿真时间。此外,提出的两个模型与传统的Markovian-Gaussian模型进行比较,结果表明,两个噪声模型有更高的精度和更低的复杂度,尤其是LS-SVM模型能够代替传统的Markovian-Gaussian模型,更适合用作低压电力线通信信道噪声发生器。该噪声模型的提出对研究在电力线通信系统和无线通信系统中内部和外部电磁源的电磁干扰有重要意义。
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.
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.
引文
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[15]何东超.LS-SVM模型在陆地地震随机噪声建模及压制中的作用[D].吉林:吉林大学,2015.
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[17]靖永志,何飞,张昆仑.基于RBF神经网络和LS-SVM组合模型的磁浮车间隙传感器温度补偿[J].电工技术学报,2016,31(15):73-80.Jing Yongzhi,He Fei,Zhang Kunlun.Temperature compensation of Maglev train gap sensor based on RBF neural network and LS-SVM combined model[J].Transactions of China Electrotechnical Society,2016,31(15):73-80.
[18]Suykens J A K,Brabanter J,Lukas L.Weighted least squares support vector machines:robustness and sparse approximation[J].Neuro Computing,2002,48(1):85-105.
[19]邓蕊,马永军,刘尧猛.基于改进交叉验证算法的支持向量机多类识别[J].天津科技大学学报,2007,22(2):58-61.Deng Rui,Ma Yongjun,Liu Yaomeng.Support vector machine multi-class classification based on an improved cross validation algorithm[J].Journal of Tianjin University of Science&Technology,2007,22(2):58-61
[20]Enqing Chen,Ran Tao,Xinghao Zhao,Channel equalization for OFDM system based on the BP neural network[C]//2006 8th International Conference on Signal Processing,Beijing,China,2006,DOI:10.1109/ICOSP.2006.345910.
[21]李龙,魏靖,黎灿兵,等.基于人工神经网络的负荷模型预测[J].电工技术学报,2015,30(8):225-230.Li Long,Wei Jing,Li Canbing,et al.Prediction of load model based on artificial neural network[J].Transactions of China Electrotechnical Society,2015,30(8):225-230.
[22]马永涛.通信信道建模的神经网络优化技术研究[D].天津:天津大学,2009.
[23]Clerc M,Kennedy J.The particle swarm-explosion,stability and convergence in a multidimensional complex space[J].IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
[24]Zhang Q.Using wavelet network in nonparametric estimation[J].IEEE Transactions on Neural Networks,1997,8(2):227-236.
[25]Zimmermann M,Dostert K.Analysis and modeling of impulsive noise in broad-band powerline communications[J].IEEE Transactions on Electromagnetic Compatibility,2002,44(1):249-258.
[2]卢文冰,张慧,赵雄文,等.网络参数对低压宽带电力线信道的影响[J].电工技术学报,2016,31(增刊1):221-229.Lu Wenbing,Zhang Hui,Zhao Xiongwen,et al.Research on the effect of network parameters for outdoor low-voltage broadband power line channels[J].Transactions of China Electrotechnical Society,2016,31(S1):221-229.
[3]谷志茹,刘宏立,詹杰,等.智能电网窄带OFDM通信系统噪声抑制[J].电工技术学报,2014,29(11):269-276.Gu Zhiru,Liu Hongli,Zhan Jie,et al.Noise suppression investigation of a narrowband power line communication for smart grid[J].Transactions of China Electrotechnical Society,2014,29(11):269-276.
[4]樊邦奎,丁冠军,兰海滨,等.智能电网应用中的PLC最短路径及多径传输统计模型[J].电工技术学报,2013(增刊2):387-390.Fan Bangkui,Ding Guanjun,Lan Haibin,et al.Research on the shortest path and multi-path transmission statistical model of PLC in smart grid[J].Transactions of China Electrotechnical Society,2013(S2):387-390.
[5]Gotz M,Rapp M,Dostert K.Power line channel characteristics and their effect on communication system design[J].IEEE Communications Magazine,2004,42(4):78-86.
[6]姜霞,Nguimbis J,程时杰.低压配电网载波通信噪声特性研究[J].中国电机工程学报,2000,20(11):30-35.Jiang Xia,Nguimbis J,Cheng Shijie.Noise characteristics investigation in low voltage power line communication[J].Proceedings of the CSEE,2000,20(11):30-35.
[7]Laguna-Sanchez G,Lopez-Guerrero M.On the use of alpha-stable distributions in noise modeling for PLC[J].IEEE Transactions on Power Delivery,2015,30(4):1863-1870.
[8]郭昊坤,吴军基,应展烽,等.一种改进的马尔科夫链及其在电力线通信信道脉冲噪声建模中的应用[J].电力系统保护与控制,2012,40(5):129-132.Guo Haokun,Wu Junji,Ying Zhanfeng,et al.Application of improved Markov chain in impulse noise modeling of power line communication channel[J].Power System Protection and Control,2012,40(5):129-132.
[9]Meng H,Guan Y L,Chen S.Modeling and analysis of noise effects on broadband power-line communications[J].IEEE Transactions on Power Delivery,2010,20(2):630-637.
[10]Dubey A,Mallik R K,Schober R.Performance analysis of a power line communication system employing selection combining in correlated log-normal channels and impulsive noise[J].Iet Communications,2014,8(7):1072-1082.
[11]Katayama M,Yamazato T,Okada H.A mathematical model of noise in narrowband power line communication systems[J].IEEE Journal on Selected Areas in Communications,2006,24(7):1267-1276.
[12]郭昊坤,吴军基,应展烽,等.电力线通信信道噪声测量与特性分析[J].电力系统通信,2011,32(11):1-3.Guo Haokun,Wu Junji,Ying Zhanfeng,et al.Measurement and analysis on channel noise of power line communication[J].Telecommunications for Electric Power System,2011,32(11):1-3.
[13]Gianaroli F,Pancaldi F,Sironi E,et al.Statistical modeling of periodic impulsive noise in indoor power-line channels[J].IEEE Transactions on Power Delivery,2012,27(27):1276-1283.
[14]Zimmermann M,Dostert K.Analysis and modeling of impulse noise in broadband powerline communications[J].IEEE Transactions on Electromagnetic Compatibility,2002,44(1):249-258.
[15]何东超.LS-SVM模型在陆地地震随机噪声建模及压制中的作用[D].吉林:吉林大学,2015.
[16]Suykens J A K,Vandewalle J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
[17]靖永志,何飞,张昆仑.基于RBF神经网络和LS-SVM组合模型的磁浮车间隙传感器温度补偿[J].电工技术学报,2016,31(15):73-80.Jing Yongzhi,He Fei,Zhang Kunlun.Temperature compensation of Maglev train gap sensor based on RBF neural network and LS-SVM combined model[J].Transactions of China Electrotechnical Society,2016,31(15):73-80.
[18]Suykens J A K,Brabanter J,Lukas L.Weighted least squares support vector machines:robustness and sparse approximation[J].Neuro Computing,2002,48(1):85-105.
[19]邓蕊,马永军,刘尧猛.基于改进交叉验证算法的支持向量机多类识别[J].天津科技大学学报,2007,22(2):58-61.Deng Rui,Ma Yongjun,Liu Yaomeng.Support vector machine multi-class classification based on an improved cross validation algorithm[J].Journal of Tianjin University of Science&Technology,2007,22(2):58-61
[20]Enqing Chen,Ran Tao,Xinghao Zhao,Channel equalization for OFDM system based on the BP neural network[C]//2006 8th International Conference on Signal Processing,Beijing,China,2006,DOI:10.1109/ICOSP.2006.345910.
[21]李龙,魏靖,黎灿兵,等.基于人工神经网络的负荷模型预测[J].电工技术学报,2015,30(8):225-230.Li Long,Wei Jing,Li Canbing,et al.Prediction of load model based on artificial neural network[J].Transactions of China Electrotechnical Society,2015,30(8):225-230.
[22]马永涛.通信信道建模的神经网络优化技术研究[D].天津:天津大学,2009.
[23]Clerc M,Kennedy J.The particle swarm-explosion,stability and convergence in a multidimensional complex space[J].IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
[24]Zhang Q.Using wavelet network in nonparametric estimation[J].IEEE Transactions on Neural Networks,1997,8(2):227-236.
[25]Zimmermann M,Dostert K.Analysis and modeling of impulsive noise in broad-band powerline communications[J].IEEE Transactions on Electromagnetic Compatibility,2002,44(1):249-258.