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An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time

机译:改进的基于粒子群算法的可变采样时间最大功率点跟踪策略

摘要

This paper presents an improved maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems based on particle swarm optimization (PSO). The capability of the PSO algorithm to cope with partially shaded conditions (PSCs) is the primary motivation of this research. Unlike conventional PSO-based MPPT systems, a variable sampling time strategy (VSTS) based on the investigation of the dynamic behavior of converter current is deployed to increase system tracking time. The performance of the proposed system is evaluated using MATLAB simulation and experimentation, in which a digital signal controller is used to implement the proposed algorithm on a real boost converter connected to a PV simulator. The main advantage of the proposed algorithm is fast and accurate performance under different conditions, including PSCs. (C) 2014 Elsevier Ltd. All rights reserved.
机译:本文提出了一种改进的基于粒子群优化(PSO)的光伏(PV)系统最大功率点跟踪(MPPT)策略。 PSO算法应对部分阴影条件(PSC)的能力是本研究的主要动机。与传统的基于PSO的MPPT系统不同,部署了基于对转换器电流的动态行为进行调查的可变采样时间策略(VSTS),以增加系统跟踪时间。使用MATLAB仿真和实验评估了所提出系统的性能,其中使用数字信号控制器在连接到PV模拟器的实际升压转换器上实现所提出的算法。所提出算法的主要优点是在包括PSC在内的不同条件下的快速准确的性能。 (C)2014 Elsevier Ltd.保留所有权利。

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