Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm-Reference-Cited by-同舟云学术

Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm

Published:2023-10-18 Issue:20 Volume:13 Page:11409
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Ahmed Ali Agoub Ramadan¹^ORCID,Hançerlioğullari Aybaba²,Rahebi Javad³^ORCID,Lopez-Guede Jose Manuel⁴^ORCID

Affiliation:

1. Department of Material Science and Engineering, University of Kastamonua, Kastamonu 37150, Turkey

2. Department of Physics, University of Kastamonu, Kastamonu 37150, Turkey

3. Department of Software Engineering, Istanbul Topkapi University, Istanbul 34087, Turkey

4. Department of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain

Abstract

The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study’s innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains.

Funder

Vitoria-Gasteiz Mobility Lab Foundation

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/20/11409/pdf

Reference20 articles.

1. State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques;Wazirali;Electr. Power Syst. Res.,2023

2. Global renewable energy resources;Energy Sources,2006

3. A comprehensive study of battery-supercapacitor hybrid energy storage system for standalone PV power system in rural electrification;Jing;Appl. Energy,2018

4. A comparative study on maximum power point tracking techniques for photovoltaic power systems;Subudhi;IEEE Trans. Sustain. Energy,2012

5. A comprehensive review and classified comparison of MPPT algorithms in PV systems;Sarvi;Energy Syst.,2022

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Comparative Study of a Buck DC-DC Converter Controlled by the MPPT (P&O) Algorithm without or with Fuzzy Logic Controller;Applied Sciences;2024-08-29

2. An MPPT Control Strategy Based on Current Constraint Relationships for a Photovoltaic System with a Battery or Supercapacitor;Energies;2024-08-11

3. Dynamic PI-PD cascaded MPPT controller for SPV system with battery charging circuit;Engineering Research Express;2024-07-02

4. Implementation of Fuzzy Logic Control on the ESP32 Microcontroller for an Automatic Infusion Monitoring System;2023 International Conference on Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA);2023-11-24

5. Optimal Allocation of DG Units in Radial Distribution Systems;2023 8th International Conference on Mathematics and Computers in Sciences and Industry (MCSI);2023-10-14