摘要
针对传统最大功率跟踪技术容易陷入局部最大功率点的问题,提出用1种改进的蝴蝶优化算法来实现部分遮荫条件下光伏阵列的最大功率输出。为解决传统蝴蝶算法收敛速度慢、搜索过程震荡大的问题,引入收敛因子,使全局搜索速度加快。将蝴蝶算法与细菌觅食算法相结合,对蝴蝶位置更新函数进行优化——根据细菌觅食的翻滚、游动行为对位置更新方程进行调整改进,赋予增量步长以较大的系数比重,以加快算法收敛速度、提高算法局部寻优能力。在太阳光照恒定和光照突变2种环境下进行了仿真对比实验。结果证明,所提算法在光照不均匀及光照突变条件下均能快速、稳定地在线寻得全局最大功率点。
Aiming at the problem that the traditional maximum power tracking technology is easy to fall into the local maximum power point,an improved butterfly optimization algorithm is proposed to achieve the maximum power output of the photovoltaic system under partial shading conditions.Aiming at the problem that the convergence speed of the traditional butterfly algorithm is slow and the search process is volatile,the convergence factor is introduced to speed up the global search.The butterfly position upgrade function is optimized by combining butterfly algorithms with bacterial foraging algorithms,according to the rolling and swimming behavior of bacterial foraging,the position update equation is adjusted and improved,and the increamental stop size is given a larger coefficient proportion to accelerate the convergence speed of the algorithm and improve the local optimization.In addition,the algorithm simulation comparison experiment is carried out in the two environments of sunlight constant and light mutation,and the results show that the proposed algorithm can quickly and stably find the global maximum power point online under the conditions of uneven illumination and light mutation.
作者
郑佳钰
干树川
李静
ZHENG Jiayu;GAN Shuchuan;LI Jing(School of Automation and Information Engineering,Sichuan University of Science&Engineering,Zigong 643000,China;Artificial Intelligence Key Laboratory of Sichuan Province,Yibin 644000,China)
出处
《电力科学与工程》
2023年第3期1-8,共8页
Electric Power Science and Engineering
基金
四川省科技厅科技项目(2020JDJQ0075)
人工智能四川省重点实验室科研项目(2019RYJ08)。
关键词
太阳能发电
最大功率跟踪
蝴蝶优化算法
收敛因子
细菌觅食算法
solar power
generation maximum power point tracking
butterfly optimization algorithm
convergence factor
bacterial foraging algorithms
