A Grey Wolf-Assisted Perturb & Observe MPPT Algorithm for a PV System
A Grey Wolf-Assisted Perturb & Observe MPPT Algorithm for a PV System
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DOI：
10.1109/tec.2016.2633722
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发表时间：
2017-03-01
影响因子：
4.9
通讯作者：
Ray, Pravat Kumar
中科院分区：
文献类型：
--
作者：
Mohanty, Satyajit;Subudhi, Bidyadhar;Ray, Pravat Kumar
本文提出了一种新的混合最大功率点跟踪（MPPT）算法，结合了灰狼优化（GWO）和扰动观察（P&O）技术，可在太阳辐照度和部分阴影条件快速变化的情况下从光伏系统中有效提取最大功率。 GWO 处理 MPPT 的初始阶段，然后在最后阶段应用 P&O 算法，以更快地收敛到全局峰值 (GP)。因此，该 MPPT 克服了 Mohanty 等人之前报告的基于 GWO 的 MPPT 算法所遇到的计算开销。使用混合技术背后的想法是缩小 GWO 的搜索空间，这有助于加速实现向 GP 的收敛。所提出的 MPPT 算法首先使用 MATLAB/Simulink 实现，随后为其实际实现准备了实验装置。从获得的结果来看，与基于 GWO 和 PSO+PO 的 MPPT 算法相比，所提出的 MPPT 在任何天气条件下都能提供卓越的跟踪性能。
This paper proposes a new hybrid maximum power point tracking (MPPT) algorithm combining grey wolf optimization (GWO) and perturb & observe (P& O) technique for efficient extraction of maximum power from a photovoltaic system subjected to rapid variation of solar irradiance and partial shading conditions. GWO handles the initial stages of MPPT followed by application of the P& O algorithm at the final stage in view of achieving faster convergence to the global peak (GP). This MPPT thus overcomes the computational overhead as encountered in the case of a GWO-based MPPT algorithm reported earlier by Mohanty et al. The idea behind using the hybrid technique is to scale down the search space of GWO which helps to speed up for achieving convergence toward the GP. The proposed MPPT algorithm is first implemented using MATLAB/Simulink and subsequently an experimental setup is prepared for its practical implementation. From the obtained results, it is confirmed that the proposed MPPT provides superior tracking performance in any weather conditions compared to both GWO and PSO+PO-based MPPT algorithms.