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MPPT for PV systems based on a dormant PSO algorithm

机译：基于休眠PSO算法的光伏系统MPPT

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摘要

Since the power-voltage characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded conditions, the conventional maximum power point tracking (MPPT) control methods will fail to work. However, the particle swarm optimization (PSO) algorithm is very suitable to solve the multi-extreme optimization problem. Then this paper proposes a dual-algorithm search method: first, a dormant particle swarm optimization (DPSO) algorithm is activated to search the area of global peak, and then the algorithm will be switched to conventional incremental conductance (INC) algorithm to track the maximum output power of photovoltaic arrays. During the iteration process of DPSO, if particles happen to search repeatedly or sway in a small region, they will be turned into dormant state so as to reduce convergence time and improve efficiency. Due to the elimination of searching repeatedly, the number of particles can be large to strengthen optimization capability. In addition, the optimal number of particles for DPSO is found by analysis and simulation. Furthermore, the searching sequence of particles is optimized to effectively reduce fluctuation of voltage and suppress output voltage spike. Finally, the excellent performance of the proposed model is verified by simulations and experiments. (C) 2015 Elsevier B.V. All rights reserved.
机译：由于光伏（PV）阵列的功率-电压特性曲线在部分阴影条件下具有多个峰值，因此常规的最大功率点跟踪（MPPT）控制方法将无法工作。然而，粒子群优化算法（PSO）非常适合解决多极优化问题。然后本文提出了一种双重算法搜索方法：首先，激活一个休眠粒子群算法（DPSO）来搜索全局峰的面积，然后将该算法切换为常规增量电导（INC）算法来跟踪光伏阵列的最大输出功率。在DPSO的迭代过程中，如果粒子发生反复搜索或在小范围内摇摆，它们将变为休眠状态，从而减少了收敛时间并提高了效率。由于消除了重复搜索，因此可以增加粒子数量以增强优化能力。另外，通过分析和模拟找到了DPSO的最佳颗粒数量。此外，优化了粒子的搜索顺序以有效减少电压波动并抑制输出电压尖峰。最后，通过仿真和实验验证了所提模型的优良性能。 （C）2015 Elsevier B.V.保留所有权利。

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