【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。更多还原

【Abstract】 Concerning slow convergence speed in the later stage,low calculation accuracy and easily falling into the local optimum of basic Cuckoo Search( CS) algorithm,a Cuckoo Search based on Chaos theory( CCS) algorithm was proposed.Firstly,the chaos initialization was used to increase population diversity. Secondly,the chaos disturbance operator was introduced to the local optimal value to jump out of the premature convergence and improve the calculation accuracy. Finally,the global optimization was improved. Four single objective benchmark functions were tested. The simulation results in the best,the worst,average,median and standard deviation value show that CCS algorithm has faster convergence speed and higher convergence precision than CS algorithm. Harmonic is the vital cause of the distortion of current waveform and voltage instability. The analysis of harmonics in power quality analysis is a very important part in power system. The CCS algorithm was applied to harmonic estimation. The experimental results show that the CCS algorithm has better performance compared with the Particle Swarm Optimization( PSO) according to the analysis of harmonic current in mean value and standard deviation.更多还原