Application of PSO Algorithm Based on Recognition in MPPT Control of Photovoltaic Array

Jiaxiong Zhu, Zhigang Xiao, Cao Jing, Chang Feng
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引用次数: 1

Abstract

Under the condition of local shadow, the P-U curve of photovoltaic array presents multi peak phenomenon. Traditional MPPT algorithm is easy to fail. PSO algorithm is suitable for the optimization of complex multi extremum system, so it is applied in multi peak global MPPT. In order to solve the problem of low precision and premature in PSO algorithm, this paper proposes a new PSO algorithm based on recognition. Through the introduction of awareness, compared with the set value, the particles with better awareness will directly enter the next better iteration, while the particles with poor awareness will be replaced by their historical optimal location, which continues to maintain the search accuracy and speed in the later stage of particle swarm, so as to improve the accuracy and speed of multi peak global optimization. Through the simulation of MATLAB/Simulink, the results show that under the condition of uniform illumination and variable shadow, the particle swarm optimization algorithm based on recognition can effectively improve the convergence speed and accuracy of system optimization.
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基于识别的粒子群算法在光伏阵列最大功率控制中的应用
在局部阴影条件下,光伏阵列的P-U曲线呈现多峰现象。传统的MPPT算法容易失效。粒子群算法适合于复杂多极值系统的优化,因此将其应用于多峰全局最优解。为了解决粒子群算法精度低、早熟的问题,本文提出了一种新的基于识别的粒子群算法。通过引入意识,与设定值相比,意识较好的粒子将直接进入下一个较好的迭代,而意识较差的粒子将被其历史最优位置取代,在粒子群后期继续保持搜索精度和速度,从而提高多峰全局优化的精度和速度。通过MATLAB/Simulink仿真,结果表明,在均匀光照和变影条件下,基于识别的粒子群优化算法能有效提高系统优化的收敛速度和精度。
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