一种基于改进VPGA优化Elman神经网络的电力线通信数据处理算法
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摘要
为了提高宽带电力线通信系统的通信质量,基于宽带电力线通信系统的基本原理,构建了宽带电力线通信系统的仿真模型。以广东云浮某小区用户电表的实际采集数据作为原始数据,在500 m的四径信道模型下,分别引入了BP神经网络和Elman神经网络进行了通信质量的仿真测试。针对神经网络算法普遍存在的抗噪声性能差的缺点,提出一种基于改进VPGA优化的Elman神经网络用于电力线通信系统解映射模块的数据处理,并进行了仿真测试。实验结果表明,该算法不占用宝贵的频谱资源且实现方便,并且除去信号被噪声淹没等极端恶劣的信道环境以外,均可以显著提高宽带电力线通信系统的通信质量,降低误码率。
In order to improve the communication quality of the broadband power line communication system and based on the basic principle of the broadband power line communication system, a simulation model of the broadband power line communication system is built. Taking the actual data collected from a district user's electric meter in Yunfu of Guangdong Province as the original data, the BP neural network and Elman neural network are introduced to the simulation test of the communication quality under the 500 m four-path channel model. Aiming at the disadvantage of poor anti-noise performance of neural network algorithms, an Elman neural network based on improved VPGA optimization is proposed for the data processing of the de-mapping module of power line communication system, and the simulation test is carried out. Experimental results show that the algorithm does not occupy valuable spectrum resources and is convenient to implement. Except the extreme bad channel environment, the algorithm can significantly improve the communication quality of the broadband power line communication system and reduce the bit error rate.
In order to improve the communication quality of the broadband power line communication system and based on the basic principle of the broadband power line communication system, a simulation model of the broadband power line communication system is built. Taking the actual data collected from a district user's electric meter in Yunfu of Guangdong Province as the original data, the BP neural network and Elman neural network are introduced to the simulation test of the communication quality under the 500 m four-path channel model. Aiming at the disadvantage of poor anti-noise performance of neural network algorithms, an Elman neural network based on improved VPGA optimization is proposed for the data processing of the de-mapping module of power line communication system, and the simulation test is carried out. Experimental results show that the algorithm does not occupy valuable spectrum resources and is convenient to implement. Except the extreme bad channel environment, the algorithm can significantly improve the communication quality of the broadband power line communication system and reduce the bit error rate.
引文
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[16]曹旺斌,尹成群,谢志远,等.多输入多输出宽带电力线载波通信信道模型研究[J].中国电机工程学报,2017,37(4):1136-1142.CAO Wangbin,YIN Chengqun,XIE Zhiyuan,et al.Research on broadband MIMO power line communications model[J].Proceedings of the CSEE,2017,37(4):1136-1142.
[17]TONELLO A M,VERSOLATTO F,BEJAR B,et al.Afitting algorithm for random modelling the PLCchannel[J].IEEE Transactions on Power Delivery,2012,27(3):1477-1484.
[18]TONELLO A M,VERSOLATTO F,PITTOLO A.In-home power line communication channel:statistical characterization[J].IEEE Transactions on Communications,2014,62(6):2096-2106.
[19]张旭辉,张礼勇,梁宵.基于改进粒子群优化算法的电力线通信多径传输模型参数辨识[J].电网技术,2009,33(1):75-79.ZHANG Xuhui,ZHANG Liyong,LIANG Xiao.Parameter identification of multi-path transmission model for power line communication based on improved particle swarm optimization[J].Power System Technology,2009,33(1):75-79.
[20]王一珺,贾嵘.基于Elman和实测风速功率数据的短期风功率预测[J].高压电器,2017,53(9):125-129.WANG Yijun,JIA Rong.Short-term wind power forecasting based on Elman neural network and measured wind speed power data[J].High Voltage Apparatus,2017,53(9):125-129.
[2]田洪迅,王宏刚,万涛,等.基于BP神经网络的配电网可靠性关联因素灵敏度计算方法[J].电力系统保护与控制,2017,45(19):71-77.TIAN Hongxun,WANG Honggang,WAN Tao,et al.Amethodology of computing sensitivity of distribution system reliability with respect to driving factors based on a BP neural network[J].Power System Protection and Control,2017,45(19):71-77.
[3]孙云莲,罗卫华,李洪.基于EMD的ICA方法在电力载波通信信号提取中的应用[J].中国电机工程学报,2007,27(16):109-113.SUN Yunlian,LUO Weihua,LI Hong.Extract signals of power line communication by a novel method based on EMD and ICA[J].Proceedings of the CSEE,2007,27(16):109-113.
[4]翟明岳,苏岭东.基于EMD-TFPF算法的电力线通信噪声消除技术研究[J].电力系统保护与控制,2015,43(7):51-56.ZHAI Mingyue,SU Lingdong.A noise mitigation method based on EMD-TFPF in powerline communication system[J].Power System Protection and Control,2015,43(7):51-56.
[5]苏岭东,翟明岳,何欣.基于时频峰值滤波的电力线通信噪声消除方法[J].电力系统保护与控制,2015,43(1):15-20.SU Lingdong,ZHAI Mingyue,HE Xin.A new noise mitigation method based on time frequency peak filtering in powerline communication system[J].Power System Protection and Control,2015,43(1):15-20.
[6]LE Jian,WANG Cao,ZHOU Wu,et al.A novel PLCchannel modeling method and channel characteristic analysis of a smart distribution grid[J].Protection and Control of Modern Power Systems,2017,2(2):146-158.DOI:10.1186/s41601-017-0044-2.
[7]刘晓胜,崔莹,徐殿国.低压电力线通信组网性能优化方法[J].电力自动化设备,2017,37(12):16-21,42.LIU Xiaosheng,CUI Ying,XU Dianguo.Performance optimization for low voltage power line communication[J].Electric Power Automation Equipment,2017,37(12):16-21,42.
[8]TLICH M,ZEDDAM A,MOULIN F,et al.Indoor power-line communications channel characterization up to 100 MHz-part II:time-frequency analysis[J].IEEETransactions on Power Delivery,2008,23(3):1402-1409.
[9]索超男,张慧,赵雄文.小波基在低压电力线信道有色背景噪声建模中的应用研究[J].电力系统保护与控制,2017,45(4):121-125.SUO Chaonan,ZHANG Hui,ZHAO Xiongwen.Research on the application of wavelet basis functions on modeling of colored background noise for low-voltage power line channels[J].Power System Protection and Control,2017,45(4):121-125.
[10]LIU Yue,YANG Chuanchuan,LI Hongbin.Pseudo pilot-aided OFDM system with high spectral efficiency for high performance coaxial transmission[J].Science China(Information Sciences),2017,60(6):191-199.
[11]肖沈阳,金志刚,苏毅珊,等.一种优化的g OMP稀疏OFDM信道估计方法[J].工程科学与技术,2017,49(5):149-155.XIAO Shenyang,JIN Zhigang,SU Yishan,et al.An Optimized gOMP algorithm for sparse OFDM channel estimation[J].Engineering Science and Technology,2017,49(5):149-155.
[12]唐英杰,董月军,任宏亮,等.基于时频域卡尔曼滤波的CO-OFDM系统相位噪声补偿算法[J].光学学报,2017,37(9):47-58.TANG Yingjie,DONG Yuejun,REN Hongliang,et al.Phase noise compensation algorithm based on Kalman filtering in time and frequency domains for CO-OFDMsystem[J].Acta Optica Sinica,2017,37(9):47-58.
[13]薛艳明,彭云柯,高飞.基于压缩感知的OFDM稀疏信道估计导频优化算法[J].北京理工大学学报,2017,37(5):511-514.XUE Yanming,PENG Yunke,GAO Fei.Optimized pilot placement for compressive sensing based on sparse channel estimation in OFDM systems[J].Transactions of Beijing Institute of Technology,2017,37(5):511-514.
[14]刘晓胜,刘佳生,孙贺,等.用于电力线通信系统的OFDM定时同步算法[J].电力自动化设备,2018,38(1):179-183.LIU Xiaosheng,LIU Jiasheng,SUN He,et al.OFDMtiming synchronization algorithm for power line communication system[J].Electric Power Automation Equipment,2018,38(1):179-183.
[15]李松浓,胡晓锐,郑可,等.低压电力线载波通信信道衰减特性测量与分析[J].电力系统保护与控制,2018,46(4):99-106.LI Songnong,HU Xiaorui,ZHENG Ke,et al.Measure and research on attenuation characteristics of voltage power line communication channel[J].Power System Protection and Control,2018,46(4):99-106.
[16]曹旺斌,尹成群,谢志远,等.多输入多输出宽带电力线载波通信信道模型研究[J].中国电机工程学报,2017,37(4):1136-1142.CAO Wangbin,YIN Chengqun,XIE Zhiyuan,et al.Research on broadband MIMO power line communications model[J].Proceedings of the CSEE,2017,37(4):1136-1142.
[17]TONELLO A M,VERSOLATTO F,BEJAR B,et al.Afitting algorithm for random modelling the PLCchannel[J].IEEE Transactions on Power Delivery,2012,27(3):1477-1484.
[18]TONELLO A M,VERSOLATTO F,PITTOLO A.In-home power line communication channel:statistical characterization[J].IEEE Transactions on Communications,2014,62(6):2096-2106.
[19]张旭辉,张礼勇,梁宵.基于改进粒子群优化算法的电力线通信多径传输模型参数辨识[J].电网技术,2009,33(1):75-79.ZHANG Xuhui,ZHANG Liyong,LIANG Xiao.Parameter identification of multi-path transmission model for power line communication based on improved particle swarm optimization[J].Power System Technology,2009,33(1):75-79.
[20]王一珺,贾嵘.基于Elman和实测风速功率数据的短期风功率预测[J].高压电器,2017,53(9):125-129.WANG Yijun,JIA Rong.Short-term wind power forecasting based on Elman neural network and measured wind speed power data[J].High Voltage Apparatus,2017,53(9):125-129.