一种新型的电能质量扰动信号分析的CDMSPSO-MP算法

A New CDMSPSO-MP Algorithm for Power Quality Disturbance Signal Analysis

针对匹配追踪(matching pursuit,MP)算法在检测电能质量扰动信号时存在的计算量大、重构信号质量不佳的问题,利用混沌动态多种群粒子群优化(chaos dynamic multi-swarm particle swarm optimization,CDMSPSO)算法对MP算法进行优化,提出了CDMSPSO-MP算法。首先,CDMSPSO算法使用Logistic映射替代伪随机数更新种群,提高信号重构时搜索时频原子的随机性;然后,将种群划分为多个小规模种群并设置相应的重组期,增加信号重构时频原子的多样性;最后,以扰动信号与原子内积的绝对值作为CDMSPSO算法的适应度函数,替代MP算法的遍历计算,提升信号的重构速度。实验结果表明,CDMSPSO-MP算法有效提高了计算速度,减少了无关时频原子作为扰动信号分量的计算,提高了重构信号的质量。

For the problems of the matching pursuit(MP)algorithm in the detection of power quality disturbance signals,such as large computation amount and poor reconstruction signal quality,a chaos dynamic multi-swarm particle swarm optimization(CDMSPSO)algorithm is used to optimize the MP algorithm,and the CDMSPSO-MP algorithm is proposed.Firstly,the CDMSPSO algorithm replaces pseudo-random numbers with Logistic mapping to update the population,so as to improve the randomness of searching time-frequency atoms during signal reconstruction.Then,the population is divided into multiple small-scale swarms with corresponding recombination periods to increase the diversity of time-frequency atoms during signal reconstruction.Finally,the absolute value of the inner product between the disturbance signal and the atom is used as the fitness function of the CDMSPSO algorithm to replace the traversal calculation of the MP algorithm,thereby improving the signal reconstruction speed.The experimental results show that the CDMSPSO-MP algorithm can effectively improve the calculation speed,reduce the computation of irrelevant time-frequency atoms as disturbance signal components,and improve the quality of the reconstructed signal.