摘要
为了提高光伏阵列光电转换效率,确保光伏阵列功率输出始终维持在最大功率点上,传统最大功率点跟踪算法在应用于局部阴影条件时,可能存在陷入局部最优或跟踪时间过长等问题。提出一种粒子群与细菌觅食混合算法,并将其应用于光伏阵列的最大功率点跟踪中,来改善跟踪过程中的收敛精度与速度。通过仿真实验结果,与传统扰动观察算法以及细菌觅食算法进行对比,验证了混合算法在跟踪速度、收敛精度、稳定性上的优越性,以及在动态光照条件下的适应性能力。
In order to improve the photoelectric conversion efficiency of the photovoltaic array and ensure the power output of the photovoltaic array is always maintained at the maximum power point,when the traditional maximum power point tracking algorithm is applied to partial shadow conditions,there may be problems such as falling into the local optimum or longer tracking time.The hybrid algorithm of particle swarm optimization and bacterial foraging is proposed and applied to the maximum power point tracking of photovoltaic array to improve the convergence accuracy and speed in the tracking process.Compared with traditional disturbance observation algorithm and bacterial foraging algorithm,it is verified that this hybrid algorithm is better in tracking speed,convergence accuracy,stability and adaptability under dynamic lighting conditions.
作者
支昊
张建德
黄陈蓉
薛正爱
ZHI Hao;ZHANG Jian-de;HUANG Chen-rong;XUE Zheng-ai(School of Electrical Power Engineering,Nanjing Institute of Technology,Nanjing 211167,China)
出处
《电工电气》
2021年第6期14-19,共6页
Electrotechnics Electric
基金
江苏省高等学校自然科学研究重大项目(19KJA510006)。
关键词
最大功率点跟踪
粒子群算法
细菌觅食算法
光伏阵列
maximum power point tracking
particle swarm optimization algorithm
bacterial foraging optimization algorithm
photovoltaic array
