基于SA-CPSO算法的光伏列阵多峰值最大功率点追踪的研究
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摘要
快速准确追踪到光伏列阵多峰值最大功率点具有重要的工程意义。传统粒子群算法应用于最大功率追踪时会产生稳定性较差和容易陷入局部最优解等问题,通过结合模拟退火算法能快速跳出局部最优解和混沌理论的遍历性的优点,得到模拟退火混沌粒子群算法。算法后期能快速稳定地向全局最大功率点收敛,可较好地解决光伏发电系统接收光照不均匀时多峰值最大功率追踪的问题。最后建立多组光伏列阵接收不同光照的场景,使用MATLAB仿真验证了模拟退火混沌粒子群算法在追踪最大功率点时寻优速度和收敛稳定性上的优越性。
It is of great engineering significance to track the maximum power point of the photovoltaic array quickly and accurately. Application of the traditional particle swarm optimization in maximum power point tracking would cause poor stability and likelihood of falling into local optimal solution. Combination with simulated annealing algorithm helps to jump out of local optimal solution and acquire the advantage of ergodicity of the chaos theory, thus obtaining simulated annealing chaos particle swarm optimization. Furthermore, the proposed algorithm could converge to the global maximum power point quickly and steadily, thus solving the problem of multi-peak maximum power point tracking in case of insufficient illumination reception of the photovoltaic system. Finally, scenes were built for several groups of photovoltaic arrays to receive different illuminations. MATLAB simulation results verified the superiority of the simulated annealing chaos particle swarm optimization in the respects of speed and convergence stability in its tracking of the maximum power point.
It is of great engineering significance to track the maximum power point of the photovoltaic array quickly and accurately. Application of the traditional particle swarm optimization in maximum power point tracking would cause poor stability and likelihood of falling into local optimal solution. Combination with simulated annealing algorithm helps to jump out of local optimal solution and acquire the advantage of ergodicity of the chaos theory, thus obtaining simulated annealing chaos particle swarm optimization. Furthermore, the proposed algorithm could converge to the global maximum power point quickly and steadily, thus solving the problem of multi-peak maximum power point tracking in case of insufficient illumination reception of the photovoltaic system. Finally, scenes were built for several groups of photovoltaic arrays to receive different illuminations. MATLAB simulation results verified the superiority of the simulated annealing chaos particle swarm optimization in the respects of speed and convergence stability in its tracking of the maximum power point.
引文
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[4]KASHIF I,ZAINAL S,MUHAMMAD A,et al.An improved particle swarm optimization(PSO)-based MPPT for PV with reduced steadystate oscillation[J].IEEE Transactions on Power Electronics,2012,27(8):3627-3628
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[6]邓颖,王聪.局部遮阴下粒子群算法在光伏离网系统最大功率点跟踪中的应用[J].南京信息工程大学学报,2017,9(1):106-112.
[7]程泽,董梦男,杨添剀,等.基于自适应混沌粒子群算法的光伏电池模型参数辨识[J].电工技术学报,2014,29(9):245-252.
[8]陈阳,唐培和,徐奕奕.结合混沌粒子群的分布式最大功率点跟踪[J].广西科技大学学报,2015,6(2):41-46.
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[10]杨洁,蒋林,蹇清平,等.基于模拟退火粒子群优化的光伏多峰最大功率跟踪算法[J].计算机应用,2014,7(1):330-333.
[11]王亚楠,康英伟,郑鹏远,等.基于遗传模拟退火算法的最大功率点跟踪研究[J].上海电力学院学报,2016,32(3):252-256.
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