基于改进CPSO的动态阴影环境下光伏MPPT仿真研究
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摘要
动态阴影下,传统最大功率点追踪(MPPT)算法易陷入局部极值,而常规粒子群(PSO)算法实现的MPPT控制易给系统带来较大的振荡。针对上述问题,提出一种自适应精英策略改进混沌粒子群(AEM-CPSO)算法的MPPT控制策略。该算法对粒子前三次迭代进行混沌搜索,使粒子在初始状态具有全局遍历性。自适应精英策略运用于粒子搜索后期,用于缓解算法后期振荡的问题。仿真结果表明,AEM-CPSO算法在全局搜索性,追踪速度以及暂态稳定性都优于传统方法。
Under the dynamic shadow, the traditional MPPT algorithm is easy to fall into the local extremum, while the MPPT control implemented by the conventional Particle Swarm Optimization(PSO) algorithm is easy to introduce a large shock to the system. To solve the problems above, an adaptive MPPT control strategy based on the improved Chaotic Particle Swarm Optimization(AEM-CPSO) algorithm is proposed. The algorithm performs chaotic search on the first three iterations of the particle, which makes the particle have global ergodicity in the initial state. The adaptive elite strategy is used in the late phase of particle search to mitigate the problem of late oscillation of the algorithm. Simulation results show that AEM-CPSO algorithm is superior to traditional methods in global search, tracking speed and transient stability.
Under the dynamic shadow, the traditional MPPT algorithm is easy to fall into the local extremum, while the MPPT control implemented by the conventional Particle Swarm Optimization(PSO) algorithm is easy to introduce a large shock to the system. To solve the problems above, an adaptive MPPT control strategy based on the improved Chaotic Particle Swarm Optimization(AEM-CPSO) algorithm is proposed. The algorithm performs chaotic search on the first three iterations of the particle, which makes the particle have global ergodicity in the initial state. The adaptive elite strategy is used in the late phase of particle search to mitigate the problem of late oscillation of the algorithm. Simulation results show that AEM-CPSO algorithm is superior to traditional methods in global search, tracking speed and transient stability.
引文
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[18]陈杏灿,程汉湘,彭湃,等.光伏电池的建模与光伏发电系统的仿真[J].广东电力,2016,29(2):25-29,58.CHEN Xingcan,CHENG Hanxiang,PENG Pai,et al.Modeling of photovoltaic cells and simulation of photovoltaic power generation systems[J].Guangdong Electric Power,2016,29(2):25-29,58.
[19]王琦,郑丹丹,温正赓.局部阴影下光伏阵列建模对比分析与改进[J].陕西电力,2017,45(5):13-16,63.WANG Qi,ZHENG Dandan,WEN Zhenggeng.Comparative analysis and improvement of photovoltaic array modeling under local shadow[J].Shaanxi Electric Power,2017,45(5):13-16,63.
[20]原敬磊,赵李宏,李明,等.光伏发电系统功率输出控制模式研究[J].智慧电力,2017,45(11):91-95.YUAN Jinglei,ZHAO Lihong,LI Ming,et al.Research on power output control mode of photovoltaic power generation system[J].Smart Power,2017,45(11):91-95.
[21]郑伟,陈仕彬,梁福波,等.基于PSO-PSA的光伏系统最大功率点跟踪技术[J].热力发电,2018,47(2):76-84.ZHENG Wei,CHEN Shibin,LIANG Fubo,et al.Maximum power point tracking technology for photovoltaic systems based on PSO-PSA[J].Thermal Power Generation,2018,47(2):76-84.
[22]张经纬,归一数,康英伟,等.基于改进粒子群算法的锅炉再热蒸汽温度模型辨识[J].热力发电,2017,46(7):72-78.ZHANG Jingwei,GUI Yishu,KANG yingwei,et al.Model identification of boiler reheat steam temperature based on improved particle swarm optimization algorithm[J].Thermal Power Generation,2017,46(7):72-78.
[23]李善寿,张兴,张鸿恺,等.基于功率闭环控制与PSO算法的全局MPPT方法[J].中国电机工程学报,2014,34(28):4809-4816.LI Shanshou,ZHANG Xing,ZHANG Hongkai,et al.Global MPPT method based on power closed-loop control and PSO algorithm[J].Proceedings of the CSEE,2014,34(28):4809-4816.
[24]王秀云,田壁源.基于自适应可控开关矩阵的光伏阵列优化配置策略[J].电力系统保护与控制,2017,45(17):77-83.WANG Xiuyun,TIAN Biyuan.Optimized configuration strategy of photovoltaic array based on adaptive controllable switch matrix[J].Power System Protection and Control,2017,45(17):77-83.
[25]张永革,石季英,张文,等.复杂遮阴条件下光伏系统MPPT控制改进PSO算法仿真研究[J].中国电机工程学报,2014,34(增刊1):39-46.ZHANG Yongge,SHI Jiying,ZHANG Wen,et al.Simulation study of improved PSO algorithm for MPPTcontrol of photovoltaic system under complicated shading condition[J].Proceedings of the CSEE,2014,34(S1):39-46.
[26]贺禹强,刘故帅,肖异瑶,等.基于改进GA-PSO混合算法的变电站选址优化[J].电力系统保护与控制,2017,45(23):143-150.HE Yuqiang,LIU Gushuai,XIAO Yiyao,et al.Optimization of substation location based on improved GA-PSO hybrid algorithm[J].Power System Protection and Control,2017,45(23):143-150.
[27]朱艳伟,石新春,但扬清,等.粒子群优化算法在光伏阵列多峰最大功率点跟踪中的应用[J].中国电机工程学报,2012,32(4):42-48.ZHU Yanwei,SHI Xinchun,DAN Yangqing,et al.Application of particle swarm optimization algorithm in multi-peak maximum power point tracking of photovoltaic arrays[J].Proceedings of the CSEE,2012,32(4):42-48.
[28]魏薇,史林军,赫卫国,等.含STATCOM的风电场级多PI控制器参数优化[J].广东电力,2018,31(9):69-76.WEI Wei,SHI Linjun,HE Weiguo,et al.Parameter optimization of wind farm multi-PI controller with STATCOM[J].Guangdong Electric Power,2018,31(9):69-76.
[29]董文永,康岚兰,刘宇航,等.带自适应精英扰动及惯性权重的反向粒子群优化算法[J].通信学报,2016,37(12):1-10.DONG Wenyong,KANG Lanlan,LIU Yuhang,et al.Inverse particle swarm optimization algorithm with adaptive elite perturbation and inertia weight[J].Acta Telecom,2016,37(12):1-10.
[30]康岚兰,董文永,宋婉娟,等.无惯性自适应精英变异反向粒子群优化算法[J].通信学报,2017,38(8):66-78.KANG Lanlan,DONG Wenyong,SONG Wanjuan,et al.Non-inertial adaptive elite variation inverse particle swarm optimization algorithm[J].Journal of Communications,2017,38(8):66-78.
[2]MANICKAM C,RAMAN G P,RAMAN G R,et al.Fireworks enriched P&O algorithm for GMPPT and detection of partial shading in PV systems[J].IEEETransactions on Power Electronics,2017,32(6):4432-4443.
[3]ALIK R,JUSOH A.Modified perturb and observe(P&O)with checking algorithm under various solar irradiation[J].Solar Energy,2017,148:128-139.
[4]BALASANKAR R,ARASU G T,CHRISTY M R J S.Aglobal MPPT technique invoking partitioned estimation and strategic deployment of P&O to tackle partial shading conditions[J].Solar Energy,2017,143:73-85.
[5]SHER H A,MURTAZA A F,NOMAN A,et al.A new sensorless hybrid MPPT algorithm based on fractional short-circuit current measurement and P&O MPPT[J].IEEE Transactions on Sustainable Energy,2015,6(4):1426-1434.
[6]党选举,杨阳,姜辉,等.基于不完全微分的自适应步长扰动观察法MPPT控制[J].太阳能学报,2016,37(12):3022-3029.DANG Xuanju,YANG Yang,JIANG Hui,et al.MPPTcontrol based on adaptive step size perturbation based on incomplete differentiation[J].Chinese Journal of Solar Energy,2016,37(12):3022-3029.
[7]王书征,李先允.一种新型自适应扰动观察法在光伏发电MPPT策略中的应用[J].太阳能学报,2016,37(9):2393-2400.WANG Shuzheng,LI Xianyun.Application of a novel adaptive perturbation observation method in photovoltaic power generation MPPT strategy[J].Chinese Journal of Solar Energy,2016,37(9):2393-2400.
[8]范钦民,闫飞,张翠芳,等.基于模糊控制的光伏MPPT算法改进[J].太阳能学报,2017,38(8):2151-2158.FAN Qinmin,YAN Fei,ZHANG Cuifang,et al.Improvement of photovoltaic MPPT algorithm based on fuzzy control[J].Chinese Journal of Solar Energy,2017,38(8):2151-2158.
[9]Maximum power point tracking of PV systems under uniform irradiation condition based on Fibonacci sequence[C]//International Conference on Management Science,2010.
[10]卫东,赵磊,赖小龙.基于抛物线极值求解的太阳电池最大功率点计算与跟踪方法[J].太阳能学报,2016,37(8):1925-1930.WEI Dong,ZHAO Lei,LAI Xiaolong.Maximum power point calculation and tracking method for solar cells based on parabola extremum[J].Chinese Journal of Solar Energy,2016,37(8):1925-1930.
[11]聂晓华,王薇.混沌改进猫群算法及其在光伏MPPT中的应用[J].中国电机工程学报,2016,36(22):6103-6110.NIE Xiaohua,WANG Wei.Chaos improved cat-group algorithm and its application in photovoltaic MPPT[J].Proceedings of the CSEE,2016,36(22):6103-6110.
[12]王立华,魏学业,孟一飞,等.基于变尺度混沌优化搜索的局部阴影条件下光伏阵列MPPT研究[J].中南大学学报(自然科学版),2017(7):1790-1796.WANG Lihua,WEI Xueye,MENG Yifei,et al.Study on photovoltaic array MPPT under locally shadowed condition based on mean-scale chaotic optimization search[J].Journal of Central South University(Science and Technology),2017(7):1790-1796.
[13]ISHAQUE K,SALAM Z,TAHERI H,et al.Maximum power point tracking for PV system under partial shading condition via particle swarm optimization[C]//2011IEEE Applied Power Electronics Colloquium(IAPEC),April 18-19,2011,Johor Bahru,Malaysia:5-9.
[14]李星硕,文辉清.基于β参数的变步长MPPT控制研究[J].电力系统保护与控制,2016,44(17):58-63.LI Xingshuo,WEN Huiqing.Research on variable step size MPPT control based onβparameters[J].Power System Protection and Control,2016,44(17):58-63.
[15]王雨,胡仁杰.基于粒子群优化和爬山法的MPPT算法[J].太阳能学报,2014,35(1):149-153.WANG Yu,HU Renjie.MPPT algorithm based on particle swarm optimization and hill-climbing method[J].Chinese Journal of Solar Energy,2014,35(1):149-153.
[16]丁爱华,卢子广,卢泉,等.基于改进PSO的复杂环境下光伏MPPT控制[J].太阳能学报,2015,36(2):408-413.DING Aihua,LU Ziguang,LU Quan,et al.Photovoltaic MPPT control in complex environment based on improved PSO[J].Chinese Journal of Solar Energy,2015,36(2):408-413.
[17]吴志程,江智军,杨晓辉.一种基于功率闭环控制的改进全局MPPT方法[J].电力系统保护与控制,2018,46(1):57-62.WU Zhicheng,JIANG Zhijun,YANG Xiaohui.An improved global MPPT method based on power closed loop control[J].Power System Protection and Control,2018,46(1):57-62.
[18]陈杏灿,程汉湘,彭湃,等.光伏电池的建模与光伏发电系统的仿真[J].广东电力,2016,29(2):25-29,58.CHEN Xingcan,CHENG Hanxiang,PENG Pai,et al.Modeling of photovoltaic cells and simulation of photovoltaic power generation systems[J].Guangdong Electric Power,2016,29(2):25-29,58.
[19]王琦,郑丹丹,温正赓.局部阴影下光伏阵列建模对比分析与改进[J].陕西电力,2017,45(5):13-16,63.WANG Qi,ZHENG Dandan,WEN Zhenggeng.Comparative analysis and improvement of photovoltaic array modeling under local shadow[J].Shaanxi Electric Power,2017,45(5):13-16,63.
[20]原敬磊,赵李宏,李明,等.光伏发电系统功率输出控制模式研究[J].智慧电力,2017,45(11):91-95.YUAN Jinglei,ZHAO Lihong,LI Ming,et al.Research on power output control mode of photovoltaic power generation system[J].Smart Power,2017,45(11):91-95.
[21]郑伟,陈仕彬,梁福波,等.基于PSO-PSA的光伏系统最大功率点跟踪技术[J].热力发电,2018,47(2):76-84.ZHENG Wei,CHEN Shibin,LIANG Fubo,et al.Maximum power point tracking technology for photovoltaic systems based on PSO-PSA[J].Thermal Power Generation,2018,47(2):76-84.
[22]张经纬,归一数,康英伟,等.基于改进粒子群算法的锅炉再热蒸汽温度模型辨识[J].热力发电,2017,46(7):72-78.ZHANG Jingwei,GUI Yishu,KANG yingwei,et al.Model identification of boiler reheat steam temperature based on improved particle swarm optimization algorithm[J].Thermal Power Generation,2017,46(7):72-78.
[23]李善寿,张兴,张鸿恺,等.基于功率闭环控制与PSO算法的全局MPPT方法[J].中国电机工程学报,2014,34(28):4809-4816.LI Shanshou,ZHANG Xing,ZHANG Hongkai,et al.Global MPPT method based on power closed-loop control and PSO algorithm[J].Proceedings of the CSEE,2014,34(28):4809-4816.
[24]王秀云,田壁源.基于自适应可控开关矩阵的光伏阵列优化配置策略[J].电力系统保护与控制,2017,45(17):77-83.WANG Xiuyun,TIAN Biyuan.Optimized configuration strategy of photovoltaic array based on adaptive controllable switch matrix[J].Power System Protection and Control,2017,45(17):77-83.
[25]张永革,石季英,张文,等.复杂遮阴条件下光伏系统MPPT控制改进PSO算法仿真研究[J].中国电机工程学报,2014,34(增刊1):39-46.ZHANG Yongge,SHI Jiying,ZHANG Wen,et al.Simulation study of improved PSO algorithm for MPPTcontrol of photovoltaic system under complicated shading condition[J].Proceedings of the CSEE,2014,34(S1):39-46.
[26]贺禹强,刘故帅,肖异瑶,等.基于改进GA-PSO混合算法的变电站选址优化[J].电力系统保护与控制,2017,45(23):143-150.HE Yuqiang,LIU Gushuai,XIAO Yiyao,et al.Optimization of substation location based on improved GA-PSO hybrid algorithm[J].Power System Protection and Control,2017,45(23):143-150.
[27]朱艳伟,石新春,但扬清,等.粒子群优化算法在光伏阵列多峰最大功率点跟踪中的应用[J].中国电机工程学报,2012,32(4):42-48.ZHU Yanwei,SHI Xinchun,DAN Yangqing,et al.Application of particle swarm optimization algorithm in multi-peak maximum power point tracking of photovoltaic arrays[J].Proceedings of the CSEE,2012,32(4):42-48.
[28]魏薇,史林军,赫卫国,等.含STATCOM的风电场级多PI控制器参数优化[J].广东电力,2018,31(9):69-76.WEI Wei,SHI Linjun,HE Weiguo,et al.Parameter optimization of wind farm multi-PI controller with STATCOM[J].Guangdong Electric Power,2018,31(9):69-76.
[29]董文永,康岚兰,刘宇航,等.带自适应精英扰动及惯性权重的反向粒子群优化算法[J].通信学报,2016,37(12):1-10.DONG Wenyong,KANG Lanlan,LIU Yuhang,et al.Inverse particle swarm optimization algorithm with adaptive elite perturbation and inertia weight[J].Acta Telecom,2016,37(12):1-10.
[30]康岚兰,董文永,宋婉娟,等.无惯性自适应精英变异反向粒子群优化算法[J].通信学报,2017,38(8):66-78.KANG Lanlan,DONG Wenyong,SONG Wanjuan,et al.Non-inertial adaptive elite variation inverse particle swarm optimization algorithm[J].Journal of Communications,2017,38(8):66-78.
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