混沌布谷鸟搜索算法在谐波估计中的应用

发布时间：2018-06-27 23:27

本文选题：粒子群优化算法 + 布谷鸟搜索算法　；参考：《计算机应用》2017年01期

【摘要】：针对布谷鸟搜索(CS)算法存在后期收敛速度慢、计算精度不高和陷入局部最优等缺点,提出了混沌布谷鸟(CCS)算法。首先,通过混沌理论初始化种群来增加种群多样性;然后,对局部最优值引入混沌扰动算子来跳出早熟收敛,提高计算精度,进而完成全局优化。对4个单目标基准函数进行仿真测试,对比最优值、最差值、平均值、中位数值及标准差值,结果表明,基于CCS算法比CS算法有更快的收敛速度和更高的收敛精度。在电力系统中谐波问题成分引起电流波形畸变,电网不稳定。精确分析谐波成分是解决谐波污染的重要前提。将性能更好的CCS算法应用于谐波估计,通过比较估计均值及标准偏差,结果显示在分析谐波电流时CCS算法相比粒子群优化(PSO)算法具有更好的性能。
[Abstract]:A chaotic Cuckoo search (CCS) algorithm is proposed to overcome the shortcomings of slow convergence, low accuracy and local optimization in the Cuckoo search (CS) algorithm. Firstly, the population diversity is increased by initializing the population by chaos theory, and then chaotic perturbation operator is introduced to the local optimal value to jump out of premature convergence and improve the calculation accuracy, and then complete the global optimization. Four single objective benchmark functions are simulated and compared with the optimal value, the most differential value, the average value, the median value and the standard difference. The results show that the CCS algorithm has faster convergence speed and higher convergence accuracy than CS algorithm. The harmonic components in power system cause distortion of current waveform and instability of power network. Accurate analysis of harmonic components is an important prerequisite to solve harmonic pollution. The better performance CCS algorithm is applied to harmonic estimation. The results show that CCS algorithm has better performance than particle swarm optimization (PSO) algorithm in analyzing harmonic current by comparing the estimated mean and standard deviation.
【作者单位】：太原科技大学电子信息工程学院;
【基金】：太原科技大学研究生科技创新项目(20145019);太原科技大学博士科研启动基金资助项目(20142003)~~
【分类号】：TP18

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