Development of Cuckoo Search MPPT Algorithm for Partially Shaded Solar PV SEPIC Converter

被引:37
|
作者
Basha, C. H. Hussaian [1 ]
Bansal, Viraj [1 ]
Rani, C. [1 ]
Brisilla, R. M. [1 ]
Odofin, S. [2 ]
机构
[1] VIT Univ, Sch Elect Engn, Vellore, Tamil Nadu, India
[2] Univ Derby, Sch Energy & Environm, Derby, England
来源
SOFT COMPUTING FOR PROBLEM SOLVING, SOCPROS 2018, VOL 1 | 2020年 / 1048卷
关键词
CS MPPT; Duty cycle; PV cell and; Partial shading;
D O I
10.1007/978-981-15-0035-0_59
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Photovoltaic (PV) power generation is playing a prominent role in rural power generation systems due to its low operating and maintenance cost. The output properties of solar PV mainly depend on solar irradiation, temperature, and load impedance. Hence, the operating point of solar PV oscillates. Due to the oscillatory behavior of operating point, it is difficult to transform maximum power from the source to load. To maintain the operating point constant at the maximum power point (MPP) without oscillations, a maximum power point tracking (MPPT) technique is used. Under partial shading condition, the nonlinear characteristics of PV comprise of multiple maximum power points (MPPs). As a result, discovering true MPP is difficult. The traditional and neural network MPPT methods are not suitable to track the MPP because of oscillations around MPP and impreciseness in tracking under partial shading (PS) condition. Therefore, in this article, a biological intelligence cuckoo search optimization (CSO) technique is utilized to track and extract the maximum power of the solar PV at two PS patterns. MATLAB/Simulink is used to demonstrate the CSO MPPT operation on SEPIC converter.
引用
收藏
页码:727 / 736
页数:10
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