The maximum power point of PV (photovoltaic) generation moves depending on weather conditions and load. Therefore, it is significant to make sure that the panels can work at the maximum power point under MPPT (maxi...The maximum power point of PV (photovoltaic) generation moves depending on weather conditions and load. Therefore, it is significant to make sure that the panels can work at the maximum power point under MPPT (maximum power point tracking) control. However, it has the problems of low efficiency and unstable operation when panels are covered by the partial shadow. The result is that the output power may be substantially decreased. To overcome this issue, the authors propose a new plug-in operation point correction system. This system is put between PV panels and PCS (power conditioning system) in the existing PV generation system. In this paper, the experimental results describe that the output electric energy increases approximately 1.4 times as compared with the conventional system when the proposed correction system is inserted.展开更多
针对在局部阴影条件下,光伏阵列的功率-电压特性曲线呈现多个峰值,传统群体智能优化存在收敛速度慢、振荡幅度大和易陷入局部最优等问题,提出一种基于PSO-GWO(Particle Swarm Optimization-Grey Wolf Optimization)算法的MPPT(Maximum P...针对在局部阴影条件下,光伏阵列的功率-电压特性曲线呈现多个峰值,传统群体智能优化存在收敛速度慢、振荡幅度大和易陷入局部最优等问题,提出一种基于PSO-GWO(Particle Swarm Optimization-Grey Wolf Optimization)算法的MPPT(Maximum Power Point Tracking)控制方法。该算法引入余弦规律变化的收敛因子,平衡GWO算法的全局搜索与局部搜索能力;引入PSO算法,提高灰狼个体与自身经验之间的信息交流。仿真结果表明,提出的PSO-GWO算法在局部阴影条件下不仅能快速收敛,而且功率输出震荡幅度更小,有效提升了局部遮阴条件下光伏阵列的最大功率跟踪效率和精度。展开更多
在光伏发电系统中,光伏组件会受到实时变化光照强度的影响而处于局部阴影下,光伏组件的输出特性曲线呈现多峰值状态分布,传统的最大功率点跟踪方法(maximum power tracking,MPPT)会失效,造成系统输出功率的损失。本文提出一种改进的MPP...在光伏发电系统中,光伏组件会受到实时变化光照强度的影响而处于局部阴影下,光伏组件的输出特性曲线呈现多峰值状态分布,传统的最大功率点跟踪方法(maximum power tracking,MPPT)会失效,造成系统输出功率的损失。本文提出一种改进的MPPT算法,该算法通过改变粒子群算法(particle swarm algorithm,PSO)的惯性系数和两个学习因子,使其随着迭代进行非线性动态变化,同时引入变异策略,增强算法的全局寻优能力,达到了提升搜索精度和速度的目的。在Matlab/Simulink中建立了仿真模型,验证了改进的粒子群算法在随机光照强度能有效保证输出功率最大。展开更多
文摘The maximum power point of PV (photovoltaic) generation moves depending on weather conditions and load. Therefore, it is significant to make sure that the panels can work at the maximum power point under MPPT (maximum power point tracking) control. However, it has the problems of low efficiency and unstable operation when panels are covered by the partial shadow. The result is that the output power may be substantially decreased. To overcome this issue, the authors propose a new plug-in operation point correction system. This system is put between PV panels and PCS (power conditioning system) in the existing PV generation system. In this paper, the experimental results describe that the output electric energy increases approximately 1.4 times as compared with the conventional system when the proposed correction system is inserted.
文摘针对在局部阴影条件下,光伏阵列的功率-电压特性曲线呈现多个峰值,传统群体智能优化存在收敛速度慢、振荡幅度大和易陷入局部最优等问题,提出一种基于PSO-GWO(Particle Swarm Optimization-Grey Wolf Optimization)算法的MPPT(Maximum Power Point Tracking)控制方法。该算法引入余弦规律变化的收敛因子,平衡GWO算法的全局搜索与局部搜索能力;引入PSO算法,提高灰狼个体与自身经验之间的信息交流。仿真结果表明,提出的PSO-GWO算法在局部阴影条件下不仅能快速收敛,而且功率输出震荡幅度更小,有效提升了局部遮阴条件下光伏阵列的最大功率跟踪效率和精度。
文摘在光伏发电系统中,光伏组件会受到实时变化光照强度的影响而处于局部阴影下,光伏组件的输出特性曲线呈现多峰值状态分布,传统的最大功率点跟踪方法(maximum power tracking,MPPT)会失效,造成系统输出功率的损失。本文提出一种改进的MPPT算法,该算法通过改变粒子群算法(particle swarm algorithm,PSO)的惯性系数和两个学习因子,使其随着迭代进行非线性动态变化,同时引入变异策略,增强算法的全局寻优能力,达到了提升搜索精度和速度的目的。在Matlab/Simulink中建立了仿真模型,验证了改进的粒子群算法在随机光照强度能有效保证输出功率最大。