基于自适应粒子群算法在光伏阵列多峰值系统MPPT的控制
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摘要
在局部阴影的条件下,由于光伏阵列的P-U曲线会存在多个峰值点,传统的扰动观测方法不能快速追踪到最大功率点。本文对粒子群算法的设计参数、执行流程等方面进行优化,提出了一种基于自适应粒子群算法对光伏并网系统MPPT的控制策略,最后进行了Matlab/Simulink仿真。结果表明,该控制策略可以快速、准确地搜寻到最大功率点,能够抑制复杂条件下环境因素的影响,提高算法的跟踪精度和光伏电池的整体工作效率。
For that there are multiple peak points in the P-U curve of the PV array under the condition of local shadow, the traditional disturbance observation method cannot quickly track the maximum power point problem.This paper optimizes the design parameters and execution flow of particle swarm optimization. A control strategy based on adaptive particle swarm optimization(PSO) algorithm in photovoltaic grid-connected system MPPT is proposed. Finally, Matlab/Simulink simulation and experimental research are carried out. The result shows that the control strategy can quickly and accurately find the maximum power point. The control strategy can suppress the influence of environmental factors under complex conditions, the tracking accuracy of the algorithm and the overall working efficiency of the photovoltaic cell are improved.
For that there are multiple peak points in the P-U curve of the PV array under the condition of local shadow, the traditional disturbance observation method cannot quickly track the maximum power point problem.This paper optimizes the design parameters and execution flow of particle swarm optimization. A control strategy based on adaptive particle swarm optimization(PSO) algorithm in photovoltaic grid-connected system MPPT is proposed. Finally, Matlab/Simulink simulation and experimental research are carried out. The result shows that the control strategy can quickly and accurately find the maximum power point. The control strategy can suppress the influence of environmental factors under complex conditions, the tracking accuracy of the algorithm and the overall working efficiency of the photovoltaic cell are improved.
引文
[1]吴海涛,孙以泽,孟婥.粒子群优化模糊控制器在光伏发电系统最大功率跟踪中的应用[J].中国电机工程学报,2011,31(6):52-57.
[2]杨佳霖.基于粒子群算法的分布式能源系统容量优化配置[J].分布式能源,2017,2(6):46-51.
[3]张异殊.改进粒子群算法在光伏阵列多峰值MPPT中的应用[J].分布式能源,2018,3(1):34-38.
[4]王启业.基于改进PSO算法光伏并网系统MPPT控制策略[J].电力电子技术,2017,51(7):30-32.
[5]文穂,洪添胜,李震,等.太阳能硅光电池最大功率点跟踪算法的仿真及试验[J].农业工程学报,2012,28(1):196-201.
[6]张小莲,李群,殷明慧,等.一种引入停止机制的改进爬山算法[J].中国电机工程学报,2012,32(14):128-134.
[7]潘湘高,杨民生,敖章洪.MATLAB在控制系统时域分析中的应用[J].湖南文理学院学报(自然科学版),2016,28(3):83-87.
[8]刘翼.光伏并网发电系统建模仿真[D].北京:北京交通大学,2011.
[9]艾欣,许佳佳.基于互动调度的微网与配电网协调运行模式研究[J].电力系统保护与控制,2013,41(1):143-149.
[10]罗诚安,于惠钧,李林,等.局部阴影条件下基于MMC换流器的光伏并网[J].湖南工业大学学报,2017,31(2):49-55.
[11]刘国璧,袁宏俊.人工蜂群算法优化LS-SVM的预测模型[J].湖南文理学院学报(自然科学版),2018,30(2):27-29.
[2]杨佳霖.基于粒子群算法的分布式能源系统容量优化配置[J].分布式能源,2017,2(6):46-51.
[3]张异殊.改进粒子群算法在光伏阵列多峰值MPPT中的应用[J].分布式能源,2018,3(1):34-38.
[4]王启业.基于改进PSO算法光伏并网系统MPPT控制策略[J].电力电子技术,2017,51(7):30-32.
[5]文穂,洪添胜,李震,等.太阳能硅光电池最大功率点跟踪算法的仿真及试验[J].农业工程学报,2012,28(1):196-201.
[6]张小莲,李群,殷明慧,等.一种引入停止机制的改进爬山算法[J].中国电机工程学报,2012,32(14):128-134.
[7]潘湘高,杨民生,敖章洪.MATLAB在控制系统时域分析中的应用[J].湖南文理学院学报(自然科学版),2016,28(3):83-87.
[8]刘翼.光伏并网发电系统建模仿真[D].北京:北京交通大学,2011.
[9]艾欣,许佳佳.基于互动调度的微网与配电网协调运行模式研究[J].电力系统保护与控制,2013,41(1):143-149.
[10]罗诚安,于惠钧,李林,等.局部阴影条件下基于MMC换流器的光伏并网[J].湖南工业大学学报,2017,31(2):49-55.
[11]刘国璧,袁宏俊.人工蜂群算法优化LS-SVM的预测模型[J].湖南文理学院学报(自然科学版),2018,30(2):27-29.