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Investigation on Photovoltaic Array Modeling and the MPPT Control Method under Partial Shading Conditions

机译:局部遮阳条件下光伏阵列建模与MPPT控制方法的研究

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On the basis of a five-parameter photovoltaic (PV) mathematical model, a multipeak output model of a PV array under partial shading conditions (PSCs) is obtained by MATLAB simulation. Simulation and experimental results demonstrate that the model can simulate the performance curves of the PV array under the PSCs. Optimized particle swarm optimization (OPSO) is used to control the multipeak output model that can quickly and accurately track the global maximum power point (GMPP) of PV modules under PSCs. Its main idea is to determine the initial position of particles and remove the acceleration factor and random number in traditional particle swarm optimization (PSO) algorithm. Additionally, according to the distance between two consecutive peak points, the maximum value of velocity is obtained. The advantages of the OPSO include the following: compared with the traditional PSO algorithm, the computing time is greatly shortened; and it is easy to achieve the MPPT with a low-cost microprocessor. In addition, a PV optimizer is designed to improve the output power of PV modules under PSCs, and simulation and experimentation have compared the output characteristics of PV modules in traditional control mode and optimized control mode under PSCs. The experimental results show that the PV optimizer improves the output power of the PV modules by 13.4% under the PSC.
机译:基于五参数光伏(PV)数学模型,通过MATLAB仿真获得局部阴影条件(PSC)下的PV阵列的多跳输出模型。模拟和实验结果表明,该模型可以模拟PSC下PV阵列的性能曲线。优化的粒子群优化(OPSO)用于控制Multipak输出模型,可以快速准确地跟踪PSC下PV模块的全局最大功率点(GMPP)。其主要思想是确定粒子的初始位置,并在传统粒子群优化(PSO)算法中除去加速因子和随机数。另外,根据两个连续峰值之间的距离,获得速度的最大值。 OPSO的优点包括以下内容:与传统的PSO算法相比,计算时间大大缩短;使用低成本的微处理器易于实现MPPT。此外,PV优化器旨在改善PSC下PV模块的输出功率,并将仿真和实验与PSCS下的传统控制模式和优化的控制模式进行了比较了PV模块的输出特性。实验结果表明,PV优化器在PSC下提高了PV模块的输出功率13.4%。

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