Research on Deep Learning Based Fault Diagnosis and Prediction Methods for Power Systems
Guangyu Liu, Xinying Qum, Dongzhe Qu · Frontiers in artificial intelligence and applications · 2024
The deep learning-based power system fault diagnosis and prediction method combines efficient data acquisition techniques and advanced machine learning algorithms, aiming to improve the accuracy and response speed of fault prediction. By analyzing a large amount of historical and real-time data, the study is able to effectively identify potential fault risks and pinpoint the causes of faults. Experimental results show that the accuracy reaches 98.9% during normal operation. The method shows high efficiency and stability under various environments and conditions, which verifies its feasibility and effectiveness in practical applications. This study is of great value to improve the reliability and efficiency of power systems, and has a positive impact on the development of smart grids.