Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems

INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Par...

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Main Authors: Lei Shi, Zongyu Zhang, Yongrui Yu, Chun Xie, Tongbin Yang
Format: Article
Language:English
Published: European Alliance for Innovation (EAI) 2024-11-01
Series:EAI Endorsed Transactions on Energy Web
Subjects:
Online Access:https://publications.eai.eu/index.php/ew/article/view/7325
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author Lei Shi
Zongyu Zhang
Yongrui Yu
Chun Xie
Tongbin Yang
author_facet Lei Shi
Zongyu Zhang
Yongrui Yu
Chun Xie
Tongbin Yang
author_sort Lei Shi
collection DOAJ
description INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Partial shading (PS) conditions pose additional challenges, leading to multiple peaks in the power-voltage (P-V) curve and reduced output power. Therefore, controlling MPPT under partial shading conditions is a complex task. OBJECTIVES: This study aims to introduce a novel MMPT algorithm based on the ant colony incorporated bald eagle search optimization (AC-BESO) method to enhance the efficiency of PV systems. METHODS: The effectiveness of the proposed MPPT algorithm was established through a series of experiments using MATLAB software, tested under various levels of solar irradiance. RESULTS: Compared to existing methods, the proposed AC-BESO algorithm stands out for its simplicity in implementation and reduced computational complexity. Furthermore, its tracking performance surpasses that of conventional methods, as validated through comparative analyses. CONCLUSION: This study confirms the efficacy of the AC-BESO method over traditional strategies. It serves as a framework for selecting an MPPT approach when designing PV systems.
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issn 2032-944X
language English
publishDate 2024-11-01
publisher European Alliance for Innovation (EAI)
record_format Article
series EAI Endorsed Transactions on Energy Web
spelling doaj-art-ace7130b968744758a13bd3d013ef4a72025-08-20T02:49:46ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2024-11-011210.4108/ew.7325Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation SystemsLei Shi0Zongyu Zhang1Yongrui Yu2Chun Xie3Tongbin Yang4Guizhou Power Grid Co.Guizhou Power Grid Co.Guizhou Power Grid Co.Guizhou Power Grid Co.Guizhou Power Grid Co.INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Partial shading (PS) conditions pose additional challenges, leading to multiple peaks in the power-voltage (P-V) curve and reduced output power. Therefore, controlling MPPT under partial shading conditions is a complex task. OBJECTIVES: This study aims to introduce a novel MMPT algorithm based on the ant colony incorporated bald eagle search optimization (AC-BESO) method to enhance the efficiency of PV systems. METHODS: The effectiveness of the proposed MPPT algorithm was established through a series of experiments using MATLAB software, tested under various levels of solar irradiance. RESULTS: Compared to existing methods, the proposed AC-BESO algorithm stands out for its simplicity in implementation and reduced computational complexity. Furthermore, its tracking performance surpasses that of conventional methods, as validated through comparative analyses. CONCLUSION: This study confirms the efficacy of the AC-BESO method over traditional strategies. It serves as a framework for selecting an MPPT approach when designing PV systems. https://publications.eai.eu/index.php/ew/article/view/7325PV SystemsMPPTIrradiationPower outputAnt-colony integrated bald eagle search optimization (AC-BESO)
spellingShingle Lei Shi
Zongyu Zhang
Yongrui Yu
Chun Xie
Tongbin Yang
Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems
EAI Endorsed Transactions on Energy Web
PV Systems
MPPT
Irradiation
Power output
Ant-colony integrated bald eagle search optimization (AC-BESO)
title Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems
title_full Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems
title_fullStr Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems
title_full_unstemmed Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems
title_short Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems
title_sort research on a new maximum power tracking algorithm for photovoltaic power generation systems
topic PV Systems
MPPT
Irradiation
Power output
Ant-colony integrated bald eagle search optimization (AC-BESO)
url https://publications.eai.eu/index.php/ew/article/view/7325
work_keys_str_mv AT leishi researchonanewmaximumpowertrackingalgorithmforphotovoltaicpowergenerationsystems
AT zongyuzhang researchonanewmaximumpowertrackingalgorithmforphotovoltaicpowergenerationsystems
AT yongruiyu researchonanewmaximumpowertrackingalgorithmforphotovoltaicpowergenerationsystems
AT chunxie researchonanewmaximumpowertrackingalgorithmforphotovoltaicpowergenerationsystems
AT tongbinyang researchonanewmaximumpowertrackingalgorithmforphotovoltaicpowergenerationsystems