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Review on Classical and Emerging Maximum Power Point Tracking Algorithms for Solar Photovoltaic Systems

Document Type : Research Article

Authors

1 Department of Electrical Engineering, University Institute of Technology, HPU, Shimla, India.

2 Renewable Energy Lab, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia.

10.30501/jree.2024.407775.1650

Abstract

The sun serves as the primary energy source, providing our planet with the essential energy for sustaining life. To efficiently harness this energy, photovoltaic cells, commonly known as PV cells, are employed. These cells convert the solar energy they receive into electrical energy. The operational point of the solar cell, delivering maximum output power, is referred to as the maximum power point (MPP). However, as light availability and temperature fluctuate throughout the day, the MPP also varies accordingly. To maintain constant operation at the MPP, Maximum Power Point Tracking (MPPT) algorithms are employed to trace the MPP during module operation. These algorithms can be categorized into four groups: classical, intelligent, optimization, and hybrid, based on the tracking algorithm utilized. Each MPPT algorithm, existing in these categories, comes with its own set of advantages and limitations. This paper extensively reviews fifteen algorithms categorized under different groups. The review concludes with a comparative analysis of these algorithms, considering various parameters such as cost, complexity, tracking accuracy, and sensed parameters in a succinct manner. The paper focuses on elucidating the necessity of MPPT algorithms, their classification as per existing literature, and a comparative assessment of the studied MPPT algorithms. This comprehensive review aims to address advancements in this field, paving the way for further research.

Keywords

Main Subjects

References
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Journal of Renewable Energy and Environment
Volume 11, Issue 2
April 2024
Pages 18-29
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History
  • Receive Date: 22 August 2023
  • Revise Date: 21 November 2023
  • Accept Date: 03 January 2024
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