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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
S. N. Chaitra
NMAM Institute of Technology (NMAMIT)
Control & Systems Engineering Electrical & Electronics Engineering

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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 B. Logeshwary Dondapati Ravi Kishore K. Venkata Kishore S. Saahithi T. Vijay Muni Thandava Krishna Sai Pandraju V. Suresh