A Review on Neural Network-Assisted Boost Converter for Enhanced Voltage Gain in Solar PV Systems

Abstract

Solar photovoltaic (PV) systems are increasingly adopted as sustainable energy sources; however, their inherently low output voltage and sensitivity to environmental variations limit their effectiveness in high-voltage applications. Conventional boost converters often fail to provide sufficient voltage gain efficiently, while isolated converter topologies increase cost, size, and complexity. This paper proposes an Artificial Neural Network (ANN)-controlled interleaved boost converter integrated with a Maximum Power Point Tracking (MPPT) algorithm to enhance voltage gain and overall system efficiency. A Radial Basis Function Neural Network (RBFNN) is employed to dynamically track the maximum power point under fluctuating irradiance and temperature conditions. The interleaved converter topology minimizes current ripple, distributes thermal stress, and reduces switching losses, resulting in improved reliability and performance. MATLAB/Simulink simulations demonstrate superior voltage amplification, reduced harmonic distortion, and improved dynamic response compared to conventional control methods. The proposed system offers a cost-effective and adaptive solution suitable for high-voltage renewable energy applications such as electric vehicles, grid-connected PV systems, and energy storage integration. This research highlights the potential of intelligent control techniques in advancing high-performance solar energy conversion systems.

Introduction

The document discusses the growing need for efficient solar photovoltaic (PV) systems due to increasing energy demand, fossil fuel depletion, and environmental concerns. Although PV systems are sustainable, they produce low and variable voltage, making them unsuitable for high-voltage applications without power conditioning.

To address this, DC–DC boost converters are used to step up voltage, but conventional designs suffer from high losses, voltage stress, ripple, and poor efficiency under dynamic conditions. Non-isolated high-gain converters using interleaved structures, coupled inductors, and voltage multiplier cells are explored as better alternatives because they improve voltage gain and efficiency while reducing stress and ripple.

A major challenge in PV systems is Maximum Power Point Tracking (MPPT), which ensures optimal power extraction under changing sunlight and temperature. Traditional methods like Perturb & Observe and Incremental Conductance are simple but slow and inefficient under fast environmental changes. Intelligent approaches such as Artificial Neural Networks (ANN), fuzzy logic, and deep learning offer faster and more accurate tracking, with better adaptability to nonlinear PV behavior.

The paper proposes an ANN-based interleaved boost converter using a Radial Basis Function Neural Network (RBFNN) for MPPT. The system is modeled in MATLAB/Simulink and aims to achieve high voltage gain, improved efficiency, reduced ripple, and better dynamic performance. Interleaving reduces current stress and harmonic distortion, while ANN improves real-time tracking of maximum power.

Literature shows strong progress in high-gain converter design and AI-based MPPT, but key gaps remain: high system complexity, lack of real-time hardware validation, and limited integration of intelligent control with converter design. The proposed work addresses these by combining both converter topology and ANN-based control.

Conclusion

This review highlights the growing importance of high-voltage gain power conversion techniques in improving the performance and applicability of solar photovoltaic (PV) systems. Conventional boost converters, although simple and cost-effective, are limited by low voltage gain, high switching losses, and reduced efficiency under dynamic operating conditions. Advanced converter topologies such as interleaved boost converters, coupled inductors, and voltage multiplier configurations have demonstrated significant improvements in voltage gain, ripple reduction, and thermal performance. Additionally, intelligent Maximum Power Point Tracking (MPPT) techniques, particularly those based on Artificial Neural Networks (ANN) and hybrid approaches, have shown superior adaptability, faster tracking, and improved energy extraction compared to traditional methods. Despite these advancements, challenges remain in terms of circuit complexity, computational requirements, and real-time implementation. The integration of intelligent control with high-gain converter topologies presents a promising direction for achieving efficient, reliable, and cost-effective PV systems. Future research should focus on hardware validation, optimization of ANN models, and hybrid renewable energy integration to enhance system robustness and support next-generation smart grid and electric vehicle applications.

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Copyright

Copyright © 2026 Mr. Akash Dyaneshwar Dhok, Dr. Bhumaiah Jula. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.