Artificial Intelligence Based Multiport Converter For Seamless Integration Of Hybrid Energy Sources

This paper presents a multi-port converter (MPC) designed for efficient battery charging and power delivery through multiple output ports using various power sources, including solar PV arrays and the AC grid. The study explores three distinct battery charging modes: PV-only charging, hybrid PV-grid charging, and grid-only charging, each optimized for different conditions. Maximum Power Point Tracking (MPPT) techniques, Power Factor Correction (PFC), and Proportional-Resonant (PR) controllers ensure stable voltage, minimal ripple, and low total harmonic distortion (THD). Additionally, the MPC supports four output port operation modes, enabling flexible power distribution to high-voltage (500V) and low-voltage (36V) loads. Results demonstrate the system’s ability to maintain stable voltage and current levels, smooth mode transitions, and efficient power management under varying source availability. The proposed control strategy, including an Artificial Neural Network (ANN) controller, ensures reliable performance highlighting the system's modularity and adaptability.

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Publication Details

Published
2025-07-12
DOI
https://doi.org/10.1109/imsa65733.2025.11167654
Primary Topic
Multilevel Inverters and Converters
Type
article
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article

Artificial Intelligence Based Multiport Converter For Seamless Integration Of Hybrid Energy Sources

Mohamed-Salem Ahmed, Alaa A. Mahmoud, Ahmed A. Hafez
Multilevel Inverters and Converters
article

Artificial Intelligence Based Multiport Converter For Seamless Integration Of Hybrid Energy Sources

Mohamed-Salem Ahmed, Alaa A. Mahmoud, Ahmed A. Hafez
article en

Abstract

This paper presents a multi-port converter (MPC) designed for efficient battery charging and power delivery through multiple output ports using various power sources, including solar PV arrays and the AC grid. The study explores three distinct battery charging modes: PV-only charging, hybrid PV-grid charging, and grid-only charging, each optimized for different conditions. Maximum Power Point Tracking (MPPT) techniques, Power Factor Correction (PFC), and Proportional-Resonant (PR) controllers ensure stable voltage, minimal ripple, and low total harmonic distortion (THD). Additionally, the MPC supports four output port operation modes, enabling flexible power distribution to high-voltage (500V) and low-voltage (36V) loads. Results demonstrate the system’s ability to maintain stable voltage and current levels, smooth mode transitions, and efficient power management under varying source availability. The proposed control strategy, including an Artificial Neural Network (ANN) controller, ensures reliable performance highlighting the system's modularity and adaptability.

Assiut University (EG), Sohag University (EG)
Openalex Percentile: Top 74%
Multilevel Inverters and Converters
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