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多尺度稀疏电能质量扰动识别方法

朱云芳 吴志宇 高岩 侯怡爽 刘正杰

朱云芳, 吴志宇, 高岩, 侯怡爽, 刘正杰. 多尺度稀疏电能质量扰动识别方法[J]. 西南交通大学学报, 2020, 55(1): 18-26. doi: 10.3969/j.issn.0258-2724.20180606
引用本文: 朱云芳, 吴志宇, 高岩, 侯怡爽, 刘正杰. 多尺度稀疏电能质量扰动识别方法[J]. 西南交通大学学报, 2020, 55(1): 18-26. doi: 10.3969/j.issn.0258-2724.20180606
ZHU Yunfang, WU Zhiyu, GAO Yan, HOU Yishuang, LIU Zhengjie. Recognition Method for Multi-scale Sparse Power Quality Disturbance[J]. Journal of Southwest Jiaotong University, 2020, 55(1): 18-26. doi: 10.3969/j.issn.0258-2724.20180606
Citation: ZHU Yunfang, WU Zhiyu, GAO Yan, HOU Yishuang, LIU Zhengjie. Recognition Method for Multi-scale Sparse Power Quality Disturbance[J]. Journal of Southwest Jiaotong University, 2020, 55(1): 18-26. doi: 10.3969/j.issn.0258-2724.20180606

多尺度稀疏电能质量扰动识别方法

doi: 10.3969/j.issn.0258-2724.20180606
基金项目: 国家重点研发计划(2017YFB1201001);国家自然科学基金(51307144)
详细信息
    作者简介:

    朱云芳(1973—),女,副教授,研究方向为压缩感知、电能质量与信号识别,E-mail:zhuyunfang@home.swjtu.edu.cn

  • 中图分类号: V221.3

Recognition Method for Multi-scale Sparse Power Quality Disturbance

  • 摘要: 针对传统电能质量扰动识别中存在数据量大、扰动特征依赖主观选择的问题,提出一种多尺度稀疏电能质量扰动深度识别方法. 首先,构建电能质量的多尺度稀疏模型,通过对扰动信号平稳小波多尺度变换获得扰动的低高频信息;然后,对其压缩采样获得降维的测量数据,并在此基础之上,应用正交匹配追踪算法求取各层稀疏系数组成稀疏向量,将稀疏向量输入深度置信网络,实现扰动的智能识别;同时,为进一步提高网络识别的准确性,采用交叉熵算法完成对网络隐含层数、学习率等参数寻优;最后,为验证所述方法的有效性,针对几类典型的单一扰动和复合扰动信号进行大量仿真试验. 结果表明:在理想环境和噪声环境下,针对七类典型单一扰动,平均识别率达到99.0%和96.71%以上;针对13类多重扰动,平均识别到达97.69%和94.62%以上.

     

  • 图 1  压缩感知理论框架

    Figure 1.  Theoretical framework for compressed sensing

    图 2  RBM结构

    Figure 2.  Structure of RBM

    图 3  电能质量扰动的多尺度稀疏化

    Figure 3.  Multi-scale sparse representation of power quality

    图 4  暂降信号的多尺度稀疏化过程

    Figure 4.  Multi-scale sparse process for sag signal

    图 5  深度置信网络优化流程

    Figure 5.  Optimization procedure for deep belief network

    图 6  深度置信网络逐代寻优结果

    Figure 6.  Optimization results of deep belief network at different iterations

    表  1  电能质量扰动信号数学模型

    Table  1.   Power quality mathematical model

    信号类型公式表达参数范围
    电压暂降$f(t) = \left\{{\begin{array}{*{2}{l} }\!\!\!\!{\sin (2{\text{π} } ft),\:}\;\;\;\;{t \in \left[ {0,{t_1} } \right] \cup \left[ { {t_2},T} \right]}\\\!\!\!\!{a\sin (2{\text{π} } ft),}\;\;\;\;{t \in \left[ { {t_1},{t_2} } \right]}\end{array} } \right.$$ \begin{array}{*{20}{c} }0.2 < a < 0.8\\0.01 < {t_2} - {t_1} < 1.05\end{array}$
    电压暂升$f(t) = \left\{ {\begin{array}{*{20}{l} }\!\!\!\!{\sin (2{\text{π} } ft),\:}\;\;\;\;{t \in \left[ {0,{t_1} } \right] \cup \left[ { {t_2},T} \right]}\\\!\!\!\!{a\sin (2{\text{π} } ft),}\;\;\;\;{t \in \left[ { {t_1},{t_2} } \right]}\end{array} } \right.$$\begin{array}{*{20}{c}} {1.2 < a < 1.8} \\ {0.01 < {t_2} - {t_1} < 1.05} \end{array}$
    电压中断$f(t) = \left\{ {\begin{array}{*{20}{l} }\!\!\!\!{\sin (2{\text{π} } ft),}\;\;\;\;{t \in \left[ {0,{t_1} } \right] \cup \left[ { {t_2},T} \right]}\\\!\!\!\!{a\sin (2{\text{π} } ft),}\;\;\;\;{t \in \left[ { {t_1},{t_2} } \right]}\end{array} } \right.$$\begin{array}{*{20}{c}} {0.00 < a < 0.1} \\ {0.01 < {t_2} - {t_1} < 1.05} \end{array}$
    短时谐波$f(t) = \left\{ {\begin{array}{*{20}{l}}\!\!\!\!{\sin (2{\text{π}} ft),\:}\;\;\;\;{t \in \left[ {0,{t_1}} \right] \cup \left[ {{t_2},T} \right]}\\\!\!\!\!{\displaystyle \sum\limits_{i = 1,3,5,7} {{a_i}\sin (2{\text{π}} ift)} ,\:}\;\;\;\;{t \in \left[ {{t_1},{t_2}} \right]}\end{array}} \right.$$\begin{array}{*{20}{c}} {0.05 < {a_i} < 0.3} \\ {0.01 < {t_2} - {t_1} < 1.05} \end{array}$
    暂态振荡$f(t) = \left\{ {\begin{array}{*{20}{l} }\!\!\!\!{\sin (2{\text{π} } ft),\:}\;\;\;\;{t \in [0,{t_1}] \cup [{t_2},0.16]\:}\\\!\!\!\!{\sin (2{\text{π} } ft) + a\sin (2n{\text{π} } ft){ {\rm{e} }^{ - mt} } }\;\;\;\;{t \in [{t_1},{t_2}]}\end{array} } \right.$$\begin{gathered} 0 < a < 8 \\ 0 < {t_2} - {t_1} < 0.5 \\ \end{gathered} $
    电压脉冲$f(t) = \left\{ {\begin{array}{*{20}{l} }\!\!\!\!{\sin (2{\text{π} } ft),}\;\;\;\;{t \in [0,{t_1}] \cup [{t_2},0.16]\:}\\\!\!\!\!{a\sin (2{\text{π} } ft),}\;\;\;\;{t \in [{t_1},{t_2}]}\end{array} } \right.$$\begin{array}{*{20}{c}} {2 < a} \\ {0 < {t_2} - {t_1} < 0.01} \end{array}$
    电压波动$f(t) = [1 + a \sin (2n{\text{π}} ft)] \sin (2{\text{π}} ft)$$\begin{array}{*{20}{c}} {0.1 < a < 0.3} \\ {0.000\;5 < {t_2} - {t_1} < 0.000\;6} \end{array}$
    正常电压$f(t) = \sin (2{\text{π}} ft)$
     注:a 为各扰动的幅值分量;t 为总时长;t1t2 为扰动起止时刻.
    下载: 导出CSV

    表  2  单一扰动深度识别结果

    Table  2.   Recognition results of single disturbance %

    信号类型SNR
    20 dB30 dB40 dB无噪声
    电压暂降 95(97) 97(97) 96(97) 98(98)
    电压中断 98(98) 98(98) 98(100) 98(100)
    短时谐波 95(96) 97(98) 97(98) 97(99)
    电压暂升 97(95) 100(99) 100(99) 99(99)
    脉冲暂态 98(96) 98(100) 100(97) 100(100)
    短时振荡 97(100) 100(100) 100(100) 100(100)
    电压闪变 98(96) 99(100) 100(100) 100(100)
    平均 96.71(96.43) 98.43(98.57) 98.71(98.86) 99.00(99.43)
     注:括号中为传统方法识别率.
    下载: 导出CSV

    表  3  多重扰动深度识别结果

    Table  3.   Recognition results for mixed disturbance %

    扰动类型SNR
    20 dB30 dB40 dB无噪声
    中断 & 谐波 98(96) 97(97) 97(91) 98(98)
    暂升 & 谐波 91(94) 91(94) 93(93) 95(95)
    暂降 & 短时振荡 98(100) 100(100) 100(99) 100(100)
    脉冲 & 谐波 89(65) 97(69) 97(80) 97(82)
    短时振荡 & 谐波 89(89) 89(96) 92(93) 95(95)
    闪变 & 谐波 92(91) 91(93) 93(95) 94(93)
    短时振荡 & 脉冲 98(100) 99(100) 100(100) 100(100)
    闪变 & 脉冲 97(92) 98(100) 100(100) 99(100)
    闪变 & 短时振荡 92(98) 99(100) 98(100) 100(100)
    谐波 & 短时振荡 & 脉冲 97(98) 100(96) 100(99) 100(99)
    谐波 & 脉冲 & 闪变 93(93) 93(94) 94(94) 94(93)
    短时振荡 & 脉冲 & 闪变 96(97) 99(99) 99(100) 98(100)
    暂降 & 短时振荡 & 脉冲 & 闪变 100(100) 100(100) 100(100) 100(100)
    平均 94.62(93.31) 96.46(95.23) 97.15(95.69) 97.69(96.54)
     注:括号中为传统方法识别率.
    下载: 导出CSV

    表  4  参数寻优结果

    Table  4.   Parameter optimization results

    扰动类型隐含层单元数/个隐含层单元数/个学习率正则化参数识别率/%
    单一扰动2031420.050 00.035 799.0
    多重扰动1031020.001 10.005 397.6
    下载: 导出CSV

    表  5  不同方法的用时对比

    Table  5.   Recognition time comparison of different methods

    方法1 000 次网络训练耗时/s识别总耗时/s
    传统方法1 132.81 765.8
    本文方法 895.41 464.7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-08-12
  • 修回日期:  2019-03-29
  • 网络出版日期:  2019-04-18
  • 刊出日期:  2020-02-01

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