基于电导增量法与改进粒子群算法混合控制的最大功率点跟踪策略
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摘要
利用传统电导增量法跟踪最大功率点时,若跟踪步长较大,则跟踪速度较快,但跟踪精度较差;反之,则跟踪精度较好,但跟踪速度较慢。当外界环境发生变化时,利用传统电导增量法得到的功率变化曲线振荡幅度较大,功率损失较多。改进粒子群算法能够对外界环境的突变迅速作出响应,利用该方法得到的功率变化曲线振荡幅度较小,但是很难精确地定位到最大功率点(MPP)。因此,文章提出一种混合控制的最大功率点跟踪(MPPT)策略,先利用改进粒子群算法快速跟踪到MPP附近,然后利用小步长电导增量法对MPP进行精细搜索。仿真结果表明,该跟踪策略在一定程度上能够增加跟踪系统的响应速度、跟踪精度,减小功率变化曲线的振荡幅度。
When the traditional incremental conductance(INC) method with a big step is used to track the maximum power point, it will generate fast tracking speed but poor tracking precision.Conversely, with a small step, it will cause slow tracking speed but good tracking accuracy. When confronted with the changing environment, the INC method has a large oscillation and power loss.The improved particle swarm optimization(PSO) algorithm can respond to the abrupt environment quickly with small power oscillation, but it is difficult to locate the MPP accurately. Therefore, a hybrid control MPPT strategy is proposed in this paper. First, the improved PSO algorithm can track the MPP vicinity quickly, and then the incremental conductance method is used to search the MPP accurately. The simulation results show that this strategy can increase the tracking system's response speed, tracking accuracy and reduce the steady oscillation to a certain extent.
When the traditional incremental conductance(INC) method with a big step is used to track the maximum power point, it will generate fast tracking speed but poor tracking precision.Conversely, with a small step, it will cause slow tracking speed but good tracking accuracy. When confronted with the changing environment, the INC method has a large oscillation and power loss.The improved particle swarm optimization(PSO) algorithm can respond to the abrupt environment quickly with small power oscillation, but it is difficult to locate the MPP accurately. Therefore, a hybrid control MPPT strategy is proposed in this paper. First, the improved PSO algorithm can track the MPP vicinity quickly, and then the incremental conductance method is used to search the MPP accurately. The simulation results show that this strategy can increase the tracking system's response speed, tracking accuracy and reduce the steady oscillation to a certain extent.
引文
[1]金薇.太阳能电池最大功率点跟踪技术研究[J].电子科技,2015,28(3):150-153.
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[7]钟黎萍,张水平,顾启民.基于基因排序遗传算法的串联光伏组件MPPT研究[J].可再生能源,2017,35(3):384-388.
[8]李倩,周彬倩,张建成,等.基于自适应差分进化和BP神经网络的光伏功率预测[J].陕西电力,2014,42(2):24-27.
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[10]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.
[11]Ishaque K,Salam Z,Shamsudin A,et al.A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm[J].Applied Energy,2012,99(11):414-422.
[12]傅望,周林,郭珂,等.光伏电池工程用数学模型[J].电工技术学报,2011,26(10):211-216.
[13]Ishaque K,Salam Z,et al.A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition[J].IEEE Transactions on Industrial Electronics,2013,60(8):3195-3206.
[2]Esram Trishan,Chapman Patrick L.Comparison of photovoltaic array maximum power point tracking techniques[J].IEEE Transactions on Energy Conversion,2007,22(2):439-449.
[3]钟长艺,康龙云,聂洪涛,等.基于开路电压法光伏电池最大功率追踪器[J].电力电子技术,2011,45(7):103-105.
[4]戴欣平,马广,杨晓红.太阳能发电变频器驱动系统的最大功率追踪控制法[J].中国电机工程学报,2005,25(8):95-99.
[5]Abdelsalam A K,Massoud A M,Ahmed S,et al.Highperformance adaptive perturb and observe MPPTtechnique for photovoltaic-based microgrids[J].IEEETransactions on Power Electronics,2011,26(4):1010-1021.
[6]胡茗哲,马学军.一种基于电导增量法的可变步长MPPT算法的研究[J].湖北理工学院学报,2013,29(4):20-23.
[7]钟黎萍,张水平,顾启民.基于基因排序遗传算法的串联光伏组件MPPT研究[J].可再生能源,2017,35(3):384-388.
[8]李倩,周彬倩,张建成,等.基于自适应差分进化和BP神经网络的光伏功率预测[J].陕西电力,2014,42(2):24-27.
[9]刘建辉,李博.局部阴影下基于遗传蚁群算法对MPPT的研究[J].可再生能源,2017,35(1):13-19.
[10]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.
[11]Ishaque K,Salam Z,Shamsudin A,et al.A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm[J].Applied Energy,2012,99(11):414-422.
[12]傅望,周林,郭珂,等.光伏电池工程用数学模型[J].电工技术学报,2011,26(10):211-216.
[13]Ishaque K,Salam Z,et al.A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition[J].IEEE Transactions on Industrial Electronics,2013,60(8):3195-3206.