| 引用本文: | 王学伟,董晓璇,王琳,袁瑞铭,田海亭,姜振宇,王国兴.m序列伪随机动态测试信号建模与压缩检测方法[J].电力自动化设备,2017,37(2): |
| WANG Xuewei,DONG Xiaoxuan,WANG Lin,YUAN Ruiming,TIAN Haiting,JIANG Zhenyu,WANG Guoxing.Modeling of m-sequence pseudo-random dynamic test signal and compressive measurement method[J].Electric Power Automation Equipment,2017,37(2): |
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| 摘要: |
| 针对智能电网中动态负荷对电能计量的影响问题，建立了m序列伪随机动态测试信号的参数模型，并分析了该测试信号的统计特性；证明了该动态测试信号的频域稀疏性，采用压缩感知理论建立了伪随机动态测试信号的压缩感知检测系统模型，采用稳态优化方法构建了压缩感知测量矩阵；在此基础上，针对m序列伪随机动态测试信号，提出了电能量值的压缩感知测量方法；仿真分析了长度为255位、511位、1023位单周期和多周期m序列动态测试信号的相对误差，误差均小于10-12，可忽略不计，表明所提压缩感知测量方法能够准确测量伪随机动态测试信号的电能量值。 |
| 关键词: 智能电网 动态负荷 m序列 伪随机测试信号 测试信号模型 压缩检测模型 压缩感知测量方法 电能测量方法 |
| DOI：10.16081/j.issn.1006-6047.2017.02.022 |
| 分类号:TP391 |
| 基金项目:国家自然科学基金资助项目(51577006)；国网冀北电力有限公司电力科学研究项目(8KE000M15006) |
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| Modeling of m-sequence pseudo-random dynamic test signal and compressive measurement method |
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WANG Xuewei1, DONG Xiaoxuan1, WANG Lin1, YUAN Ruiming2, TIAN Haiting2, JIANG Zhenyu2, WANG Guoxing2
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1.College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;2.Electric Power Research Institute, State Grid Jibei Electric Power Company Limited, Beijing 100045, China
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| Abstract: |
| Aiming at the influence of dynamic loads on the electric energy measurements in smart grid, a parameter model of m-sequence pseudo-random dynamic test signal is established, its statistic characteristics are analyzed, and its frequency-domain sparsity is then verified. A compressive sensing measurement system model of pseudo-random dynamic test signal is established based on the compressive sensing theory and the steady-state optimization method is applied to obtain the compressive sensing measurement matrix, based on which, a compressive sensing measurement method for measuring the electric energy of m-sequence pseudo-random dynamic test signal is proposed. The relative error of m-sequence dynamic test signal is simulated and analyzed for different types, single and multiple periods, as well as different lengths, 255 bits, 511 bits, and 1023 bits. Being neglectable, all the calculated relative errors are less than 10-12, which shows that, the proposed compressive sensing measurement method can accurately measure the electric energy of pseudo-random dynamic test signal. |
| Key words: smart grid dynamic loads m-sequence pseudo-random test signals test signal models compressive measurement model compressive sensing measurement method electric energy measurement method |
