Maximum power point tracking of photovoltaic system using adaptive modified firefly algorithm

N. Windarko, A. Tjahjono, D. O. Anggriawan, M. Purnomo
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引用次数: 21

Abstract

A photovoltaic (PV) module is an important source in distributed generation due to low maintenance cost, low operational cost and eco-friendly. Tracking the maximum power point (MPP) of a PV module has been a hot issue to increase energy production. Maximum power point tracking (MPPT) methods based on nature inspired algorithm such as firefly algorithm (FA) has been proposed to track the MPP. However, the problem the FA method is required long time to reach convergence. Therefore, this paper proposes an adaptive modified firefly algorithm (AMFA) to tracking faster the MPP for convergence. The proposed method is implemented on a buck converter. To evaluate the algorithm, the proposed method is compared with FA and modified FA (MFA). The proposed method is verified by PSIM simulator. The results show that the proposed method can accurately track the MPP and improve the performance of FA in tracking speed for convergence.
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基于自适应修正萤火虫算法的光伏系统最大功率点跟踪
光伏(PV)组件因其低维护成本、低运行成本和环保而成为分布式发电的重要来源。跟踪光伏组件的最大功率点(MPP)一直是提高能源产量的热点问题。提出了基于自然启发算法的最大功率点跟踪方法,如萤火虫算法(FA)。然而,该方法需要较长的收敛时间。因此,本文提出了一种自适应修正萤火虫算法(AMFA),以更快地跟踪MPP以收敛。该方法在降压变换器上实现。为了对算法进行评价,将该方法与遗传算法和改进遗传算法(MFA)进行了比较。通过PSIM仿真验证了该方法的有效性。结果表明,该方法能够准确地跟踪MPP,提高了遗传算法的跟踪收敛速度。
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