A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systems

被引:128
作者
Al-Majidi, Sadeq D. [1 ]
Abbod, Maysam F. [1 ]
A-Raweshidy, Hamed S. [1 ]
机构
[1] Brunel Univ London, Coll Engn Design & Phys Sci, Dept Elect & Comp Engn, Uxbridge UB8 3PH, Middx, England
关键词
Drift problem; Fuzzy logic (FL); Maximum power point tracking (MPPT); Perturb and observe (P&O); Photovoltaic (PV); Power tracking efficiency; CONTROL ALGORITHM; MPPT ALGORITHM; NEURAL-NETWORK; PV SYSTEM; CONTROLLER; PERTURB; STATE;
D O I
10.1016/j.ijhydene.2018.06.002
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Maximum power point tracking (MPPT) techniques are considered a crucial part in photovoltaic system design to maximise the output power of a photovoltaic array. Whilst several techniques have been designed, Perturb and Observe (P&O) is widely used for MPPT due to its low cost and simple implementation. Fuzzy logic (FL) is another common technique that achieves vastly improved performance for MPPT technique in terms of response speed and low fluctuation about the maximum power point. However, major issues of the conventional FL-MPPT are a drift problem associated with changing irradiance and complex implementation when compared with the P&O-MPPT. In this paper, a novel MPPT technique based on FL control and P&O algorithm is presented. The proposed method incorporates the advantages of the P&O-MPPT to account for slow and fast changes in solar irradiance and the reduced processing time for the FL-MPPT to address complex engineering problems when the membership functions are few. To evaluate the performance, the P&O-MPPT, FL-MPPT and the proposed method are simulated by a MATLAB-SIMULINK model for a grid-connected PV system. The EN 50530 standard test is used to calculate the efficiency of the proposed method under varying weather conditions. The simulation results demonstrate that the proposed technique accurately tracks the maximum power point and avoids the drift problem, whilst achieving efficiencies of greater than 99.6%. (C) 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:14158 / 14171
页数:14
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