Sustainable Grid Integration of PV Systems using Dual-Phase Boost Converter and Intelligent MPPT Techniques

The increasing demand for energy and environmental concerns necessitate the integration of Renewable Energy Sources (RES), particularly Photovoltaic (PV) systems, into modern power grids. This paper presents an efficient grid connected PV system utilizing a dual-phase Interleaved Boost Converter (IBC) and an intelligent Maximum Power Point Tracking (MPPT) approach based on Adaptive Neuro Fuzzy Inference System (ANFIS). To enhance the controller's performance, optimization algorithms Class Topper Optimization (CTO), Falcon Optimization Algorithm (FOA), and Pufferfish Optimization Algorithm (POA) are employed. The dual phase IBC ensures reduced ripple, improved voltage gain, and stable power conversion. Simulation results in MATLAB/Simulink demonstrate that the proposed system achieves 94% efficiency, 0.95% Total Harmonic Distortion (THD), and a maximum MPPT tracking efficiency of 97.05% using the POA-ANFIS controller.

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Publication Details

Journal
International Journal of Electrical and Electronics Research
Published
2025-08-30
DOI
https://doi.org/10.37391/ijeer.130307
Primary Topic
Photovoltaic System Optimization Techniques
Type
article
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article

Sustainable Grid Integration of PV Systems using Dual-Phase Boost Converter and Intelligent MPPT Techniques

R. Meenal, Blessy A Rahiman, J. Jayakumar
International Journal of Electrical and Electronics Research
Photovoltaic System Optimization Techniques
article

Sustainable Grid Integration of PV Systems using Dual-Phase Boost Converter and Intelligent MPPT Techniques

R. Meenal, Blessy A Rahiman, J. Jayakumar
article en

Abstract

The increasing demand for energy and environmental concerns necessitate the integration of Renewable Energy Sources (RES), particularly Photovoltaic (PV) systems, into modern power grids. This paper presents an efficient grid connected PV system utilizing a dual-phase Interleaved Boost Converter (IBC) and an intelligent Maximum Power Point Tracking (MPPT) approach based on Adaptive Neuro Fuzzy Inference System (ANFIS). To enhance the controller's performance, optimization algorithms Class Topper Optimization (CTO), Falcon Optimization Algorithm (FOA), and Pufferfish Optimization Algorithm (POA) are employed. The dual phase IBC ensures reduced ripple, improved voltage gain, and stable power conversion. Simulation results in MATLAB/Simulink demonstrate that the proposed system achieves 94% efficiency, 0.95% Total Harmonic Distortion (THD), and a maximum MPPT tracking efficiency of 97.05% using the POA-ANFIS controller.

International Journal of Electrical and Electronics ResearchVol. 13(3)
Karunya University (IN), Takenaka (Japan) (JP)
Openalex Percentile: Top 85%
Photovoltaic System Optimization Techniques
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