传统MPPT算法存在易陷入局部最优的问题,且目前采用的智能优化算法解决该类问题也有追踪精度不足、追踪速度慢等问题。为解决上述问题,该文提出一种基于金枪鱼算法（TSO）与改进黏菌觅食算法（MSMA）的混合优化算法。该方法通过早期金枪鱼算法的抛物线觅食策略来加快搜索速度,对黏菌觅食算法采用基于混沌映射的反向学习策略进行改进,达到扩大算法探索范围的目的,使之不易于陷入局部最优,并提高算法运算速度。将改进后的算法应用于光伏系统MPPT中,仿真实验结果表明：改进后算法相较于单独TSO与MSMA算法,在不同遮光条件下追踪速率有较大提升,精确度高于单独的TSO与MSMA算法,拥有更好的追踪速度与追踪精度。
Abstract
The traditional MPPT algorithm has the problem of easily falling into local optima, and the intelligent optimization algorithms currently used to solve this type of problem also have shortcomings such as insufficient tracking accuracy and slow tracking speed. To improve the above shortcomings, this article proposes a hybrid optimization algorithm based on the tuna swarm algorithm （TSO） and the multi-strategy improved slime mould algorithm（MSMA）. This method accelerates the search speed through the parabolic feeding strategy of the early tuna algorithm and improves the slime mold algorithm by using a reverse learning strategy based on chaotic mapping to expand the exploration range of the algorithm, making it less prone to falling into local optima, and improving the algorithm's operational speed. The improved algorithm is applied to the photovoltaic system MPPT, and the simulation results show that compared to the individual TSO and MSMA algorithms, the improved algorithm has a significant improvement in tracking speed under different shading conditions, with higher accuracy than the individual TSO and MSMA algorithms, and has better tracking speed and accuracy.
关键词
光伏系统 /
局部遮荫 /
最大功率点追踪 /
金枪鱼算法 /
改进黏菌觅食算法
Key words
photovoltaic system /
partial shading /
maximum power point tracking /
tuna swarm optimization /
multi-strategy improved slime mould algorithm
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基金
国家重点研发计划（2021YFB1600200）; 国家自然科学基金面上项目（12172064）; 陕西省重点研发计划（2021KW-13）
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