针对局部遮阴（PSC）时,光伏阵列输出P-U曲线出现多峰值现象,传统最大功率点追踪（MPPT）算法易陷入局部最优解,无法追踪到全局最优解的问题,该文提出基于自适应变异粒子群算法的光伏MPPT控制方法。在粒子群算法寻优过程中同步调整学习因子与惯性权重,以提高算法收敛速度与精度,同时引入变异机制以扩大粒子搜索范围,增强算法的全局寻优能力。仿真结果表明,与标准粒子群算法相比,该算法在均匀光照,静态、动态局部遮阴条件下,均能快速准确的追踪到最大功率点,其收敛速度更快,稳态精度更高。
Abstract
For the problem of partial shading condition（PSC）, there will be multi-peaks phenomenon in the output P-U curve of photovoltaic array, and the traditional maximum power point tracking（MPPT） algorithm can easily fall into local optimal solution and cannot track down the global optimal solution. Based on the above, a photovoltaic MPPT control method based on adaptive mutation particle swarm optimization is proposed in this paper. The learning factors and inertia weights are adjusted synchronously during particle swarm optimization in order to improve the convergence speed and accuracy of the algorithm. At the same time, the mutation mechanism is introduced to expand the particle search range and enhance the global optimization of the algorithm. Simulation results show that the algorithm can quickly and accurately track the maximum power point under uniform illumination, static and dynamic partial shading conditions, with faster convergence and higher steady-state accuracy than the standard particle swarm optimization.
关键词
光伏发电 /
最大功率点追踪 /
局部遮阴 /
变异粒子群算法
Key words
PV power /
maximum power point tracking /
partial shading /
mutation particle swarm optimization
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 赵书强, 王明雨, 胡永强, 等. 基于不确定理论的光伏出力预测研究[J]. 电工技术学报, 2015, 30(16): 213-220.
ZHAO S Q, WANG M Y, HU Y Q, et al. Research on the prediction of PV output based on uncertainty theory[J]. Transactions of China Electrotechnical Society, 2015, 30(16): 213-220.
[2] 李晶, 窦伟, 徐正国, 等. 光伏发电系统中最大功率点跟踪算法的研究[J]. 太阳能学报, 2007, (3): 268-273.
LI J, DOU W, XU Z G, et al. Research on MPPT methods of photovoltaic power generation system[J]. Acta energiae solaris sinica, 2007, 28(3): 268-273.
[3] 靳肖林, 文尚胜, 倪浩智, 等. 光伏发电系统最大功率点跟踪技术综述[J]. 电源技术, 2019, 43(3): 532-535.
JIN X L, WEN S S, NI H Z, et al. Review of maximum power point tracking of photovoltaic system[J]. Power technology, 2019, 43(3): 532-535.
[4] 于子捷, 魏晨曦, 田芳芳, 等. 一种改进型扰动观察法在最大功率点跟踪中的应用[J]. 电测与仪表, 2017, 54(15): 113-119.
YU Z J, WEI C X, TIAN F F, et al. An improved perturbation and observation method used in maximum power point tracking control of PV generation system[J]. Electrical measurement & instrumentation, 2017, 54(15): 113-119.
[5] 聂晓华, 赖家俊.局部阴影下光伏阵列全局最大功率点跟踪控制方法综述[J]. 电网技术, 2014, 38(12): 3279-3285.
NIE X H, LAI J J.A survey on tracking and control approaches for global maximum power point of photovoltaic arrays in partially shaded environment[J]. Power system technology, 2014, 38(12): 3279-3285.
[6] 石季英, 凌乐陶, 薛飞, 等. 领地粒子群算法在光伏最大功率跟踪的应用[J]. 太阳能学报, 2019, 40(9): 2554-2560.
SHI J Y, LING L T, XUE F, et al. Application of territotial PSO in global MPPT for PV array[J]. Acta energiae solaris sinica, 2019, 40(9): 2554-2560.
[7] 李世光, 夏杰, 李雪杨, 等. 改进APSO算法在光伏MPPT控制中的应用[J]. 电测与仪表, 2019, 56(22): 19-24.
LI S G, XIA J, LI X Y, et al. Application of improved APSO algorithm in photovoltaic MPPT control[J]. Electrical measurement & instrumentation, 2019, 56(22): 19-24.
[8] 杨海柱, 岳刚伟, 康乐.基于粒子群优化算法和电导增量法的多峰值MPPT控制[J]. 电源学报, 2019, 17(6): 128-136.
YANG H Z, YUE G W, KANG L.Multi-peak MPPT control based on PSO and INC algorithms[J]. Journal of power supply, 2019, 17(6): 128-136.
[9] 刘秋华, 张秀锦, 樊陈.基于功率差变步长扰动观察法的MPPT控制算法[J]. 电源技术, 2020, 44(7): 1035-1039.
LIU Q H, ZHANG X J, FAN C.MPPT control algorithm based on power difference variable step-size disturbance observation method[J]. Power technology, 2020, 44(7): 1035-1039.
[10] 张兴, 李善寿.局部遮挡时光伏阵列失配特性建模与分析[J]. 太阳能学报, 2014, 35(9): 1592-1598.
ZHANG X, LI S S.Modeling and analysis of mismatch characteristics of PV array under shading conditions[J]. Acta energiae solaris sinica, 2014, 35(9): 1592-1598.
[11] 韩鹏, 李银红, 何璇, 等. 结合量子粒子群算法的光伏多峰最大功率点跟踪改进方法[J]. 电力系统自动化, 2016, 40(23): 101-108.
HAN P, LI Y H, HE X, et al. Improved maximun power point tracking method for photovoltaic multi-peak based on quantum behaved particle swarm optimization algorithm[J]. Automation of electric power systems, 2016, 40(23): 101-108.
[12] 吴繁言, 李田泽, 徐亚南, 等. 基于权重指数递减粒子群算法在光伏MPPT中的应用[J]. 电测与仪表, 2019, 56(21): 81-87.
WU F Y, LI T Z, XU Y N, et al. Application of weighted index declining particle swarm optimization algorithm in photovoltaic MPPT[J]. Electrical measurement & instrumenttation, 2019, 56(21): 81-87.
[13] 陈维荣, 王伟颖, 郑义斌, 等. 局部阴影光伏发电系统中基于改进PSO的MPPT控制[J]. 西南交通大学学报, 2018, 53(6): 1095-1101, 1129.
CHEN W R, WANG W Y, ZHENG Y B, et al. MPPT control of partial shadow photovoltaic generation system based on improved PSO algorithm[J]. Journal of Southwest Jiaotong University, 2018, 53(6): 1095-1101, 1129.
[14] 王立乔, 鲍利斌, 孙孝峰.基于变异粒子群算法的光伏系统最大功率点跟踪研究[J]. 太阳能学报, 2016, 37(3): 743-751.
WANG L Q, BAO L B, SUN X F.Study of photovoltaic system maximum power point tracking based on mutation particle swarm of optimization[J]. Acta energiae solaris sinica, 2016, 37(3): 743-751.
[15] 王硕禾, 郑俊观, 陈祖成, 等. 基于改进粒子群优化算法光伏阵列多峰值MPPT的研究[J]. 可再生能源, 2019, 37(6): 879-885.
WANG S H, ZHENG J G, CHEN Z C, et al. Research on multi-peak MPPT of PV array based on improved PSO algorithm[J]. Renewable energy, 2019, 37(6): 879-885.
基金
国家青年基金（61704103）; 国网上海电力公司科技项目（H2020-073）
PDF(1753 KB)
