基于压缩感知的电能质量压缩采样重构算法

摘要

The modified compressive sampling matching pursuit (MCSMP) algorithm is proposed to solve the deficiency of the reconstruction of power quality disturbance signal based on compressive sampling matching pursuit (CoSaMP) algorithm. In the selection stage of the candidate sets, the MCSMP adopts the fuzzy threshold method instead of the fixed number compared with the CoSaMP, and uses the change of the correlation between the adjacent iterative sensing matrix and the residual error as the stop condition, which reduces the burden for the clipping of the backtrace process and the unnecessary iterations, and improves the efficiency of the algorithm. Simulation results show that:MCSMP algorithm is better than CoSaMP algorithm both in reconstruction performance and reconstruction time.%针对电能质量扰动信号的重构问题, 在压缩采样匹配追踪(Compressive sampling matching pursuit, CoSaMP) 算法的基础上, 为解决原算法的不足, 提出一种改进的压缩采样匹配追踪(Modified compressive sampling matching pursuit, MCSMP) 算法, 并将其应用在电能质量信号的重构上.该算法在候选集的选择阶段采用模糊阈值的方式代替原算法固定个数的选择方式, 并以相邻迭代感知矩阵与残差之间的相关度变化量作为算法的停止条件, 为回溯过程的剪裁减轻了负担, 避免了不必要的迭代, 提高了算法的运行效率.仿真实验结果表明:无论是重构性能指标或是重构速度, MCSMP算法的重构结果都优于CoSaMP算法.