Faults detection and classification for grid connected PV system using deep learning methods

This study provides a thorough approach for fault detection and classification in a 200 kW grid-connected photovoltaic (PV) system using Long Short-Term Memory (LSTM) neural networks. As PV systems and other renewable energy sources are being included into modern power networks, stability and reliability must be maintained. One of the biggest problems is recognizing and classifying short-circuit failures, which can result in serious damage and downtime if ignored. Conventional fault detection techniques frequently fall short in addressing their limitations in practical settings due to their inability to adjust to a variety of fault types and system dynamics. By analyzing voltage and current waveforms, the LSTM model effectively detects and classifies a wide variety of short-circuit fault types, including single-line-to-ground, line-to-line, and three-phase faults. The grid-connected PV system is simulated using Python, and synthetic fault data is generated for the training and validation of the LSTM network. By creating a solid dataset with a variety of fault scenarios, this study also fills an empirical gap and allows for a more thorough assessment of the model's performance. The simulation's results demonstrate that the LSTM model outperforms conventional techniques in terms of accuracy, defect detection rates, and response times.