Year: 2025
Type: article
Abstract: This paper proposes a deep learning-based method for power system fault diagnosis and prediction. By leveraging real-time monitoring data and deep neural networks (DNN) and convolutional neural networks (CNN), the model extracts features and accurately classifies fault patterns. Additionally, Long Short-Term Memory (LSTM) networks are used to predict potential faults by capturing long-term dependencies in sequential data. Experimental results show that the proposed method outperforms traditional techniques in accuracy, real-time performance, and robustness. Simulations and real-case studies demonstrate its effectiveness in enhancing the reliability and safety of power systems.
Author: Lehang Guo
Institution: Northeast Electric Power University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4414816781
FWCI:
Cites:
Cited by:
Related to: