摘要
电能质量扰动信号是衡量电能质量的一个重要指标,因此对电能质量扰动信号进行准确检测是提高电能质量的前提。针对传统采样方法中采样数据量大、采样时间较长以及压缩复杂度高的问题,本文基于压缩感知理论对电能质量扰动信号进行重构,首先证明电能质量扰动信号的稀疏性满足压缩感知的必备条件;采用自适应测量矩阵对电能质量扰动信号数据进行压缩采样,同时,采用谱投影梯度实现了对电能质量扰动信号的精确重构。仿真结果表明,本文采用的压缩感知恢复算法不但可以降低采样数据量和压缩复杂度,其重构误差小,压缩性能指标比较好。
Power quality disturbance signal was an important index to measure power quality, so the power quality disturbance signals should be accurately detect. To solve the shortage of large volumes of stored data and high complexity of compression in traditional method, compressed sensing was proposed to reconstruct power quality disturbance signals. The sparsity of power quality disturbance signals was proved theoretically base on DFT to satisfy the necessary conditions of compressed sensing. Adaptive measurement matrixis was used to realize compression of sampling data, and spectral projected gradient was used to reconstruct the power quality disturbance signals. The simulation results showed the proposed algorithm could not only reduce the amount of sampling and complexity of compression, but also had little reconstruction error and good compression performance.
出处
《大连工业大学学报》
CAS
北大核心
2016年第4期299-303,共5页
Journal of Dalian Polytechnic University
基金
山西省自然科学基金资助项目(2013011018-2)
关键词
压缩感知
电能质量扰动信号
自适应测量矩阵
谱投影梯度
compressed sensing
power quality disturbance signals
adaptive measurement matrix
spectral projected gradient
