| 沈跃,丁灵卫,吴翃轩,刘国海,刘慧.回溯自适应匹配追踪电能质量信号重构方法[J].电子测量与仪器学报,2017,31(5):731-738 |
| 回溯自适应匹配追踪电能质量信号重构方法 |
| Power quality signal reconstruction of backtracking based self adaptivematching pursuit method |
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| DOI：10.13382/j.jemi.2017.05.012 |
| 中文关键词: 电能质量 压缩感知 匹配追踪 自适应 回溯算法 |
| 英文关键词:power quality compressive sensing reconstruction algorithm self adaption backtracking based algorithm |
| 基金项目:国家自然科学基金（61301138）、江苏省自然科学基金（BK20130501）、江苏省博士后科研项目（1401053C）、江苏大学高级人才启动基金(10JDG136)资助项目 |
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| Author | Institution |
| Shen Yue | School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China |
| Ding Lingwei | School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China |
| Wu Hongxuan | National Electricity Science Research Institute,NARI,Nanjing 211106,China |
| Liu Guohai | School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China |
| Liu Hui | School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China |
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| 中文摘要: |
| 为改善电能质量暂稳态信号重构性能，提出了基于压缩感知理论的回溯自适应匹配追踪的电能质量信号重构方法，对电能质量信号进行处理。在自适应匹配追踪的基础上改变一个回溯步骤，初选出满足条件的候选集，然后在终选步骤中利用回溯功能，剔除系数相对较小的原子，所以该方法能够灵活地删除一些被在初选时被错误选择的原子，有着更好的稀疏信号重构和逼近性能。另外该方法无需先验信号的稀疏度，具有稀疏度自适应能力。仿真实验结果表明，10种电能质量信号的压缩重构精度都在97.63％以上，能量恢复系数要高于99.57%，信噪比高于31.42dB，均方误差百分比在2.31%之内，都能达到很好的性能指标。 |
| 英文摘要: |
| In order to improve the reconstruction performance ofpower quality transient and steady signal, a reconstruction method of power quality signal adopting backtracking based self adoptive matching pursuit algorithm is presented to conduct the power quality signals on the basis of compressive sensing theory. By adapting a simple backtracking step to SAMP method, the first step of the proposed method is to select the candidate set that meets the conditions, then by using backtracking function in final selection step, some atoms whose coefficientsare relativelysmall, so the proposed method can flexibly remove some atoms that are chosen wrongly at the previous processing and provide the batter sparse signal reconstruction and approximation performance.Furthermore, this method does not require verifysignal sparsityfirst andpossesssparsity adaptiveability. The simulation experiment results show that the reconstruction accuracy of the signal is more than 97.63%. The reconstruction result can achieve very good performance:energy recovery coefficientishigher than 99.39%, signal to noise ratiois within 1.64% and mean squared error is within 1.64%. |
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