太阳能无人机中光伏发电最大功率点跟踪算法的研究  

Research on GMPPT Tracking of Solar-powered UAV

作　　者：郁纪 肖文波[1,2] 吴华明 张华明[1] 王树鹏[3] YU Ji;XIAO Wen-bo;WU Hua-ming;ZHANG Hua-ming;WANG Shu-peng(Key Laboratory of Nondestructive Testing(Ministry of Education),Nanchang Hangkong University,Nanchang 330063,China;Jiangxi Engineering Laboratory for Optoelectronics Testing Technology,Nanchang 330063,China;AECC Shenyang Liming Aero-engine Co.,LTD.,Shenyang 110043,China)

机构地区：[1]无损检测技术教育部重点实验室(南昌航空大学),南昌330063 [2]江西省光电检测技术工程实验室,南昌330063 [3]中国航发沈阳黎明航空发动机有限责任公司,沈阳110043

出　　处：《南昌航空大学学报（自然科学版）》2023年第1期19-28,共10页Journal of Nanchang Hangkong University(Natural Sciences)

基　　金：国家自然科学基金(12064027,62065014);无损检测技术教育部重点实验室开放基金(EW201908442,EW201980090);南昌航空大学研究生创新专项(YC2020077)。

摘　　要：本文提出了变步长混沌萤火虫算法(VS-CLSFA)和免疫粒子群算法(IM-PSO),并对比了粒子群算法(PSO)、萤火虫算法(FA)及改进萤火虫算法(MFA)用于太阳能无人机光伏组件在局部阴影下最大功率点的跟踪结果。同时,研究上述算法在太阳能无人机飞行高度、速度等因素影响下的跟踪效果。研究结果表明:VS-CLSFA和IM-PSO都克服了FA、MFA和PSO陷于局部最优或者过早收敛的缺点,并且快速、稳定地追踪到太阳能无人机光伏组件产生功率的最大功率点;对于输出特性愈复杂的光伏组件,上述5种算法都需要增加迭代次数并牺牲跟踪时间来提高跟踪精度和稳定性;与VS-CLSFA相比,IM-PSO的跟踪精度提高约0.229%,跟踪时间减少约0.108 s。This paper proposes variable step size chaotic firefly algorithm(VS-CLSFA)and immune particle swarm optimization(IM-PSO),comparing particle swarm optimization(PSO),firefly algorithm(FA)and improved firefly algorithm(MFA)to the tracking results of the maximum power point of photovoltaic modules in solar UAV under partial shadow.At the same time,the tracking effects of the above algorithms under the influence of factors such as the flying height and speed of the solar UAV is studied.The results show that VS-CLSFA and IM-PSO have overcome the shortcomings of FA,MFA and PSO that are trapped in local optimal or premature convergence,and quickly and stably track the maximum power point of photovoltaic modules in solar UAV.For photovoltaic modules with more complex output characteristics,the above five algorithms need to increase the number of iterations and sacrifice tracking time to improve tracking accuracy and stability;Compared with VS-CLSFA,the tracking accuracy of IM-PSO increases by about 0.229%,and tracking time reduces by about 0.108 s.

关 键 词：太阳能无人机 光伏组件 最大功率点跟踪 组合算法 精度与速度 

分 类 号：U458[建筑科学—桥梁与隧道工程]