提出一种简化沙猫群优化算法,在复杂阴影光照环境下,辐照度从静态到动态,将该算法与粒子群算法与及灰狼算法进行对比,仿真和实验验证表明,简化沙猫群优化算法能够实现全局光伏最大功率点追踪。在相同外界条件下,相较于两种对比算法,简化沙猫群优化算法影响实验结果的控制变量少;追踪速度提升60％以上,追踪效率提升0.5％以上;且在收敛过程中功率与电压波动率较小,提高智能群体算法应用于光伏最大功率点追踪中的可靠性,并提升光伏发电利用率。
Abstract
The power-voltage output characteristic curves of PV series modules have multiple peaks when they are blocked by different shadows. The traditional maximum power point tracking algorithm has slow convergence speed and complex control parameters,so it can not achieve the global maximum power point tracking well. In this paper,a simplified sand cat swarm optimization algorithm is proposed,which is compared with particle swarm optimization algorithm and Gray Wolf algorithm respectively from static to dynamic under the complex lighting environment of shadow. Simulation and experiment verify that the simplified sand cat swarm optimization algorithm can realize the global photovoltaic maximum power point tracking. Under the same external conditions,compared with the other two algorithms,the simplified sand cat swarm optimization algorithm has fewer control variables affecting the experimental results. The tracking speed is increased by more than 60% and the tracking efficiency is increased by more than 0.5%. In addition,the power and voltage fluctuation in the convergence process is small,which improves the reliability of intelligent group algorithm applied in photovoltaic maximum power point tracking and improves the utilization rate of photovoltaic power generation.
关键词
光伏发电 /
最大功率点跟踪 /
局部遮阴 /
粒子群算法 /
沙猫群优化算法
Key words
photovoltaic power generation /
maximum power point tracking（MPPT） /
partial shading /
particle swarm optimization（PSO） /
sand cat swarm optimization algorithm（SCSO）
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基金
四川省重大科技专项（2024ZDZX0030）
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