Open Access

ARTICLE

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

Tao Hou¹, Shan Wang^1,2,*

1 School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
2 Rail Transit Electrical Automation Engineering Laboratory of Gansu Province (Lanzhou Jiaotong University), Lanzhou, 730070, China

* Corresponding Author: Shan Wang. Email:

(This article belongs to this Special Issue: Evolutionary Intelligence-Based Modelling, Optimization and Control in Renewable Energy Systems)

Energy Engineering 2023, 120(1), 87-106. https://doi.org/10.32604/ee.2022.022122

Received 22 February 2022; Accepted 29 April 2022; Issue published 27 October 2022

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 (CSA) combined with the Incremental conductance Algorithm (INC) is proposed (CSA-INC) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 52% 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). CSA-INC has an average tracking efficiency of 99.99% and an average tracking time of 0.19 s when tracking the GMPP, which improves PV power generation’s efficiency and power quality.

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