基于稀疏度特征的短时电能质量扰动信号重构方法  

Sparsity-feature based reconstruction method for short-time power quality disturbance signals

作　　者：童新 卿朝进 夏凌 郭奕 朱家龙 Tong Xin;Qing Chaojin;Xia Ling;Guo Yi;Zhu Jialong(School of Electrical Engineering and Electronic Information,Xihua University,Chengdu 610039,China)

机构地区：[1]西华大学电气与电子信息学院,成都610039

出　　处：《电测与仪表》2018年第20期114-121,共8页Electrical Measurement & Instrumentation

基　　金：四川省教育厅重点项目(15ZA0134;16ZA0154);四川省科技支撑计划项目(2015JY0138);西华大学青年学者(01201408);省部级学科平台开放课题项目(SZJJ2015-071);教育部春晖计划(Z2015113);西华大学研究生创新基金(YCJJ2017163)

摘　　要：现有基于压缩感知的短时电能质量扰动信号重构方法尚未考虑信号稀疏度特征,重构性能有待进一步提高。为此,提出一种基于稀疏度特征的信号重构方法。首先,根据压缩感知理论对信号进行采样。随后,开发出短时电能质量扰动信号的稀疏度特征—稀疏度在频域为偶数。基于该特征,提出"双步长稀疏度自适应匹配追踪"重构方法。分析与仿真结果表明,相对于传统的稀疏度自适应匹配追踪算法,提出方法降低了计算复杂度和均方误差,提高了重构信噪比和信号的正确重构概率。Based on compressed sensing,the sparsity-feature of short-time power quality disturbance signals is not considered in the existing reconstruction methods,and thus we can further improve its reconstruction performance.To this end,a sparsity-feature based signal reconstruction method is proposed in this paper.Firstly,the signals are sampled according to the compressed sensing theory.Then,the sparsity-feature of short-time power quality disturbance signals,i.e.,its sparsity in frequency-domain is even,is developed.With the developed feature,a reconstruction method referred to as double step-size sparsity adaptive matching pursuit(DS-SAMP)is proposed.Compared with the conventional sparsity adaptive matching pursuit(SAMP)algorithm,the analysis and simulation results show that the proposed method reduces the computational complexity and mean square error(MSE),as well as improves the signal-to-noise ratio(SNR)and the probability of correct reconstruction.

关 键 词：压缩感知 电能质量 稀疏度特征 双步长稀疏度自适应匹配追踪 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]