Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
-
Published:2023-04-05
Issue:7
Volume:13
Page:4617
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Mohammed Alsumaidaee Yaseen Ahmed1ORCID, Yaw Chong Tak2ORCID, Koh Siaw Paw2, Tiong Sieh Kiong2, Chen Chai Phing3, Tan Chung Hong2, Ali Kharudin4ORCID, Balasubramaniam Yogendra A. L.5
Affiliation:
1. College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University), Kajang 43000, Malaysia 2. Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Kajang 43000, Malaysia 3. Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Kajang 43000, Malaysia 4. Faculty of Electrical and Automation Engineering Technology, Kolej Universiti Tati, Kemaman 24000, Malaysia 5. TNB Research Sdn. Bhd., No. 1, Kawasan Institusi Penyelidikan, Kajang 43000, Malaysia
Abstract
Switchgear and control gear are susceptible to arc problems that arise from slowly developing defects such as partial discharge, arcing, and heating due to faulty connections. These issues can now be detected and monitored using modern technology. This study aims to explore the effectiveness of deep learning techniques, specifically 1D-CNN model, LSTM model, and 1D-CNN-LSTM model, in detecting arcing problems in switchgear. The hybrid model 1D-CNN-LSTM was the preferred model for fault detection in switchgear because of its superior performance in both time and frequency domains, allowing for analysis of the generated sound wave during an arcing event. To investigate the effectiveness of the algorithms, experiments were conducted to locate arcing faults in switchgear, and the time and frequency domain analyses of performance were conducted. The 1D-CNN-LSTM model proved to be the most effective model for differentiating between arcing and non-arcing situations in the training, validation, and testing stages. Time domain analysis (TDA) showed high success rates of 99%, 100%, and 98.4% for 1D-CNN; 99%, 100%, and 98.4% for LSTM; and 100%, 100%, and 100% for 1D-CNN-LSTM in distinguishing between arcing and non-arcing cases in the respective training, validation, and testing phases. Furthermore, frequency domain analysis (FDA) also demonstrated high accuracy rates of 100%, 100%, and 95.8% for 1D-CNN; 100%, 100%, and 95.8% for LSTM; and 100%, 100%, and 100% for 1D-CNN-LSTM in the respective training, validation, and testing phases. Therefore, it can be concluded that the developed algorithms, particularly the 1D-CNN-LSTM model in both time and frequency domains, effectively recognize arcing faults in switchgear, providing an efficient and effective method for monitoring and detecting faults in switchgear and control gear systems.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference52 articles.
1. Device for online monitoring of insulation faults in high-voltage switchgears;Song;Int. J. Distrib. Sens. Networks,2021 2. Prévé, C., Maladen, R., Dakin, G., Gentils, F., and Piccoz, D. (2019, January 3–6). Dielectric stress, design and validation of MV switchgear. Proceedings of the CIRED 2019 Conference, Madrid, Spain. 3. Silica gel mediated oxidative C–O coupling of β-dicarbonyl compounds with malonyl peroxides in solvent-free conditions;Bityukov;Pure Appl. Chem.,2017 4. Accelerated insulation aging due to fast, repetitive voltages: A review identifying challenges and future research needs;Ghassemi;IEEE Trans. Dielectr. Electr. Insul.,2019 5. Alsumaidaee, Y.A.M., Yaw, C.T., Koh, S.P., Tiong, S.K., Chen, C.P., and Ali, K. (2022). Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning. Energies, 15.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|