摘要
针对电力线载波中存在诸如变性大、衰减深、噪声干扰复杂等多种问题,提出了新型的高速载波台区识别方案。通过设计出高速载波台区识别系统架构,应用小波变换实现载波信号的识别,通过伸缩、平移高速载波信息,对运算载波信号的函数进行逐步的多尺度细化,最终实现高频载波信号处的时间细分、低频处的频率细分,进而实现自动适应时频信号分析的要求。试验表示,采用文中设计的方案能够使误差精度控制在10%以内,从技术上保证了标准化台区建设的顺利进行,全面提升了台区营销管理水平。
Aiming at various problems such as large degeneration,deep attenuation and complex noise interference in power line carrier,a new high-speed carrier area identification scheme is proposed.By designing the high-speed carrier area identification system architecture,the wavelet transform is used to realize the identification of the carrier signal.By scaling and translating the high-speed carrier information,the function of the operation carrier signal is gradually multi-scale refined,and finally the high-frequency carrier signal is realized.Time subdivision,frequency subdivision at low frequencies,and thus the requirements for automatic adaptation to time-frequency signal analysis.The test shows that the scheme designed by this paper can control the error precision within 10%,thus ensuring the smooth progress of the construction of the standardized station area and improving the marketing management level of the Taiwan Region.
作者
羡慧竹
宋玮琼
王学良
逄林
XIAN Hui-zhu;SONG Wei-qiong;WANG Xue-liang;PANG Lin(State Grid Beijing Electric Power Company,Beijing 100031,China;State Grid Info&Telecom Group China Gridcom Co.,Ltd.,Shenzhen 518031,Guangdong Province,China)
出处
《信息技术》
2020年第4期139-143,148,共6页
Information Technology
关键词
电力线载波
高速载波台区
小波变换
BP神经网络模型
power line carrier
high speed carrier station area
wavelet transform
BP neural network model
