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The IUP Journal of Electrical and Electronics Engineering

Application of Cuckoo Search Algorithm for MPP Tracking of PV Systems

Article Details

Pub. Date	: Apr, 2022
Product Name	: The IUP Journal of Electrical and Electronics Engineering
Product Type	: Article
Product Code	: IJEEE50422
Author Name	: Ritu K R* and A K Wadhwani**
Availability	: YES
Subject/Domain	: Engineering
Download Format	: PDF Format
No. of Pages	: 17

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Abstract

The paper discusses Maximum Power Point Tracking (MPPT) of PV systems using the Cuckoo Search (CS) algorithm. CS has numerous advantages, including a simple tuning procedure with great efficiency and quick convergence. In the search procedure, Cuckoo employs a random walk based on Levy flight. MPPT is compared to other techniques P & O using CS. Direct duty cycle control is used with a DC-DC converter. The findings demonstrate that CS can track MPP under various operating circumstances with fewer power losses, compared to other techniques.

Introduction

The world's focus is turning to clean and renewable energy generation as a result of environmental issues and energy challenges. The most promising renewable energy technology is solar energy generation (Mukund, 1999). PV systems grew more widespread in grid-connected applications during the previous few decades, and they played a major part in power generation in the new century (Praiselina and Belwin, 2017). When photons from the sun strike the PV array, they are instantly converted to electricity. The performance of a solar PV system is highly dependent on operational factors such as the geometric position of the sun, the ambient temperature and the sun's irradiation levels. PV systems must be run at maximum power to maximize efficiency and take advantage of Maximum Power Point Tracking (MPPT). Because the P/V and P/I properties of PV cells are nonlinear, many search techniques are often used (Praiselina and Belwin, 2017; Osisioma et al., 2017; and Fu et al., 2018). When there are fast variations in irradiance and temperature, these approaches might cause issues. Furthermore, they result in significant power losses and the inability to manage partial shade situations. Artificial neural networks (Loubna et al., 2017), fuzzy logic controller (Chian-Song, 2010), genetic algorithm (Ramaprabha et al., 2011), differential evaluation (Taheri et al., 2010), and