Tao Hou1, Shan Wang This email address is being protected from spambots. You need JavaScript enabled to view it.1, Mingxi Chen1, and Yanzhang Zhao1

1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China


 

Received: April 11, 2022
Accepted: June 6, 2022
Publication Date: August 19, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202305_26(5).0012  


ABSTRACT


The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo Search Algorithm based on the Gravitational Acceleration Mechanism (CSAGAM) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 65% compared with the traditional Cuckoo Search Algorithm (CSA), and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA). CSAGAM has an average tracking efficiency of 99.99% and an average tracking time of 0.15s when tracking the GMPP, which improves PV power generation’s efficiency and power quality.


Keywords: partial shading condition; PV system; cuckoo search algorithm; gravitational search algorithm; global maximum power point


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