Abnormal fault detection and intelligent prediction of power system based on deep learning

Qian Zhang

doi:10.1117/12.3054453

11 December 2024 Abnormal fault detection and intelligent prediction of power system based on deep learning

Qian Zhang

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 1344528 (2024) https://doi.org/10.1117/12.3054453
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

As power systems become more intricate, traditional fault detection methods have difficulty keeping pace, especially in terms of accuracy and real-time performance. The recent advancements in deep learning technology have opened up new possibilities for solving these problems. This paper, leveraging deep learning technology, examines methods for detecting and intelligently predicting abnormal faults in power systems. First, we introduce in detail the basic knowledge of deep learning and its application status in power system fault detection. Next, by building and optimizing deep learning models, this study demonstrates how to effectively identify and predict abnormal faults in power systems. Specifically, we utilize a Convolutional Neural Network (CNN) to construct a model, experimentally verifying its efficiency and accuracy under various fault scenarios. In addition, this study also explores the application potential of deep learning technology in the fields of real-time monitoring of power systems, predictive maintenance, and energy management optimization. Finally, the paper summarizes the findings and discusses existing limitations and possible directions for future research.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qian Zhang "Abnormal fault detection and intelligent prediction of power system based on deep learning", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 1344528 (11 December 2024); https://doi.org/10.1117/12.3054453

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