Using Deep Learning to Diagnose Faults in Industrial Power Systems

In this Paper, Deep Learning (DL) is utilized as an innovative method for fault detection in industrial electrical systems. Industrial electrical systems face high complexity and sensitivity, and faults in these systems can lead to financial losses and reduced productivity. By employing Convolutional Neural Networks (CNN) for image data analysis and Recurrent Neural Networks (RNN) for time-series analysis, a comprehensive approach is proposed for fault detection and classification. The proposed model was tested on both simulated and real data, showing high performance in accuracy and reduced processing time. The results indicate that deep learning can be an effective tool for enhancing the reliability of industrial electrical systems and optimizing maintenance processes.

کلیدواژه ها:

Fault Detection ، Deep Learning ، Convolutional Neural Networks (CNN) ، Recurrent Neural Networks (RNN) ، Hybrid Models (CNN-RNN)