摘要
局部遮荫条件下,标准粒子群算法跟踪光伏阵列全局最大功率点容易陷入局部最优或过早收敛等问题。因此,通过引入混沌运动、模拟退火、遗传算法等来改善标准粒子群算法(PSO),并对1×2串联光伏阵列在5种遮荫下的最大功率点进行跟踪与分析。结果表明,首先,组合算法都克服了PSO容易陷入局部最优的缺陷;其次,组合算法都存在牺牲跟踪时间来提高跟踪精度及稳定性的现象;最后,PSO中引入遗传算法的自然选择机制有助于提高跟踪速度,而混沌运动可帮助PSO提高跟踪精度及稳定性。
The standard particle swarm optimization(PSO) tracking algorithm cannot always reach maximum power point(MPP) of photovoltaic array under partial shading condition, because their convergence to the optimal operating region is not guaranteed. Therefore, the chaotic search, simulated annealing and genetic algorithm are introduced to improve PSO. Those improved algorithms are used to track maximum power point of 1×2 series photovoltaic array under five kinds of shading. Firstly, the results show that the combined algorithms overcome the problem that PSO is easy to fall into local optimal solutions. Secondly, the combined algorithms have higher tracking accuracy and stability, but slower convergence speed. Finally, the natural selection mechanism of genetic algorithm helps PSO to improve tracking speed, and the chaotic search helps PSO to improve the tracking accuracy and stability.
作者
叶国敏
肖文波
章文龙
YE Guo-min;XIAO Wen-bo;ZHANG Wen-long(Key Laboratory of Nondestructive Testing of Ministry of Education,Nanchang Hangkong University,Nanchang 330063,China;Jiangxi Engineering Laboratory for Optoelectronics Testing Technology,Nanchang Hangkong University,Nanchang 330063,China)
出处
《控制工程》
CSCD
北大核心
2022年第5期910-917,共8页
Control Engineering of China
基金
国家自然科学基金资助项目(12064027)
江西省图像处理与模式识别重点实验室开放基金资助项目(ET201908119)
无损检测技术教育部重点实验室开放基金资助项目(EW201908442,EW201980090)
南昌航空大学研究生创新专项基金资助项目(YC2019-S348)。
关键词
光伏阵列
局部遮荫
最大功率点跟踪
组合算法
Photovoltaic array
partial shading
maximum power point tracking
combined algorithm
