局部阴影下的光伏阵列MPPT算法研究
发布时间：2018-08-27 14:55
【摘要】：存在局部阴影时,光伏阵列的功率-电压(P-V)特性曲线出现多个极值点,电流-电压(I-V)特性曲线呈现阶梯状,使得基于单峰寻优的传统最大功率点跟踪(MPPT)算法失效。为此,在研究遮阴光伏阵列输出特性规律的基础上,提出了一种具有全局搜索能力的MPPT算法。该算法先用粒子群优化(PSO)算法将输入位置调整到全局最优附近,再用变步长电导增量法得到全局最优解。新的算法在减轻系统振荡和加快搜索速度方面做了改进。仿真结果表明,该方法不仅较好地克服了现有算法使用PSO大幅度随机初始化粒子位置而导致系统振荡问题,而且有效利用传统单峰寻优算法的优点,增强了系统搜索的快速性和稳定性,取得了较好的控制效果。
[Abstract]:In the presence of local shadows, the P-V characteristic curve of photovoltaic array appears several extreme points, and the current-voltage (I-V) characteristic curve presents a step shape, which makes the traditional maximum power point tracking (MPPT) algorithm based on single peak optimization invalid. Based on the study of the output characteristics of shaded photovoltaic arrays, a global search MPPT algorithm is proposed. In this algorithm, the input position is adjusted to the global optimum by particle swarm optimization (PSO) algorithm, and then the global optimal solution is obtained by the variable step size conductance increment method. The new algorithm is improved in reducing system oscillation and accelerating search speed. Simulation results show that the proposed method not only overcomes the problem of system oscillation caused by large random initialization of particle positions by using PSO, but also effectively utilizes the advantages of traditional single-peak optimization algorithms. The speed and stability of system search are enhanced, and better control effect is obtained.
【作者单位】： 浙江大学控制科学与工程学系;嘉善玛仕兰电子有限公司;
【基金】：科技型中小企业技术创新基金项目(10C26213304170)
【分类号】：TM615
[Abstract]:In the presence of local shadows, the P-V characteristic curve of photovoltaic array appears several extreme points, and the current-voltage (I-V) characteristic curve presents a step shape, which makes the traditional maximum power point tracking (MPPT) algorithm based on single peak optimization invalid. Based on the study of the output characteristics of shaded photovoltaic arrays, a global search MPPT algorithm is proposed. In this algorithm, the input position is adjusted to the global optimum by particle swarm optimization (PSO) algorithm, and then the global optimal solution is obtained by the variable step size conductance increment method. The new algorithm is improved in reducing system oscillation and accelerating search speed. Simulation results show that the proposed method not only overcomes the problem of system oscillation caused by large random initialization of particle positions by using PSO, but also effectively utilizes the advantages of traditional single-peak optimization algorithms. The speed and stability of system search are enhanced, and better control effect is obtained.
【作者单位】： 浙江大学控制科学与工程学系;嘉善玛仕兰电子有限公司;
【基金】：科技型中小企业技术创新基金项目(10C26213304170)
【分类号】：TM615
【共引文献】
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