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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Dondapati Ravi Kishore
Godavari Global University
Control & Systems Engineering Electrical & Electronics Engineering

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Advanced soft-switching high-gain Re Boost Luo converter for enhanced efficiency in photovoltaic systems Vendoti Suresh; Dondapati Ravi Kishore; T. Vijay Muni; P. Hari Krishna Prasad; Pydi Bala Krishna; A. V. G. A. Marthanda
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1177-1187

Abstract

This work presents an innovative approach to improving efficiency and performance in photovoltaic (PV) systems through the development of a soft-switching high-gain Re Boost Luo converter. This converter integrates advanced soft-switching techniques to minimize switching losses, thereby enhancing overall system efficiency, which is crucial for applications requiring substantial voltage amplification from PV sources. The Re Boost Luo converter, with its inherent high-gain capability, facilitates superior voltage conversion ratios, enabling optimal energy extraction from PV panels across varying environmental conditions. The presented converter's design focuses on reducing electromagnetic interference (EMI) and alleviating stress on switching components, thereby extending their operational lifespan and reliability. Detailed modeling and performance analysis were carried out using the MATLAB/Simulink simulation environment, which allowed for comprehensive evaluation of the converter's functionality. Simulation results confirm that the converter achieves significant improvements in voltage gain, energy conversion efficiency, and system reliability, effectively addressing common challenges associated with high-voltage PV applications. This study underscores the converter's potential to advance renewable energy technologies by providing a robust solution for high-efficiency energy conversion in PV systems.
Deep learning-based intelligent islanding detection for grid-connected photovoltaic systems using convolutional neural networks Dondapati Ravi Kishore; T. Vijay Muni; K. Venkata Kishore; V. Suresh; S. Saahithi; Thandava Krishna Sai Pandraju; S. N. Chaitra; B. Logeshwary
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1755-1767

Abstract

The increasing integration of photovoltaic (PV) systems into smart grids requires fast and reliable islanding detection to maintain grid stability and operational safety. Conventional detection methods often face challenges such as delayed response, reduced accuracy, and large non-detection zones under varying operating conditions. This paper proposes an intelligent islanding detection method for grid-connected PV systems using advanced artificial intelligence and deep learning techniques. Electrical parameters including voltage, current, frequency, and power signals are analyzed using signal processing methods and classified through a convolutional neural network (CNN) model developed in MATLAB/Simulink. Simulation results demonstrate that the proposed AI-based approach achieves rapid and accurate detection of islanding events with improved sensitivity and reduced false detections compared to conventional techniques. The proposed framework enhances the reliability, safety, and protection performance of modern photovoltaic power systems integrated with smart grids.
Co-Authors A. V. G. A. Marthanda B. Logeshwary K. Venkata Kishore P. Hari Krishna Prasad Pydi Bala Krishna S. N. Chaitra S. Saahithi T. Vijay Muni Thandava Krishna Sai Pandraju V. Suresh Vendoti Suresh