在光伏阵列受到不均匀太阳辐照时,其输出特性曲线会出现多个峰值点,常规的最大功率点跟踪方法（MPPT）可能会陷入局部峰值点,导致光伏阵列不能在最大功率点下运行。为解决此类问题,提出一种基于改进粒子群优化的灰狼算法与莱维飞行模块相结合的算法（PSO-LGWO）。该算法在函数测试和静态阴影测试中,相较于其他灰狼算法都可在保证算法跟踪精度的同时提升收敛速度;在动态阴影测试中,相较于实际光伏发电站中常见的MPPT方法,可以跳出局部最优解,且在太阳辐照度变化较大时,在保证算法跟踪精度的同时具有更快的收敛速度。
Abstract
When the photovoltaic array is subjected to uneven solar irradiation, its output characteristic curve will have multiple peak points. The conventional maximum power point tracking （MPPT） method may fall into the local peak point, resulting in that the photovoltaic array cannot operate at the maximum power point. In order to solve such problems, an algorithm based on improved particle swarm optimization combined with grey wolf algorithm and Levy flight module （PSO-LGWO） is proposed. In the function test and static shadow test, the algorithm can improve the convergence speed while ensuring the tracking accuracy of the algorithm compared with other grey wolf algorithms. In the dynamic shadow test, compared with the common MPPT method in the actual photovoltaic power station, it can jump out of the local optimal solution. When the solar irradiance changes greatly, it has faster convergence speed while ensuring the tracking accuracy of the algorithm.
关键词
最大功率点跟踪 /
太阳电池 /
太阳能发电 /
灰狼算法 /
粒子群算法
Key words
maximum power point trackers /
solar cells /
solar power generation /
grey wolf optimization（GWO） /
particle swarm optimization（PSO）
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基金
辽宁省教育厅重点攻关项目（JZL201915401）
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