Browsing by Subject "1D-CNN-LSTM"

Now showing 1 - 3 of 3
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    Publication
    Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
    (MDPI, 2023)
    Mohammed Alsumaidaee Y.A.
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    Yaw C.T.
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    Koh S.P.
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    Tiong S.K.
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    Chen C.P.
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    Tan C.H.
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    Ali K.
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    Balasubramaniam Y.A.L.
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    58648412900
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    36560884300
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    22951210700
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    15128307800
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    57883616100
    ;
    56489158400
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    36130958600
    ;
    57189520843
    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
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  • No Thumbnail Available
    Publication
    Detecting surface discharge faults in switchgear by using hybrid model
    (Institute of Advanced Engineering and Science, 2023)
    Alsumaidaee Y.A.M.
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    Koh S.P.
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    Yaw C.T.
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    Tiong S.K.
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    Chen C.P.
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    58648412900
    ;
    22951210700
    ;
    36560884300
    ;
    15128307800
    ;
    57883616100
    Switchgear plays a crucial role in power systems, providing protection and control over electrical equipment. However, tracking (surface discharge) can lead to insulation degradation and switchgear failure, necessitating reliable and effective identification of tracking defects. In this paper, we propose a hybrid one-dimension convolutional neural network long short-term memory networks (1D-CNN-LSTM) model as a solution to this problem. Data from both time domain analysis (TDA) and frequency domain analysis (FDA) are utilized for model evaluation. The model achieved error-free accuracy of 100% in both TDA and FDA during the training, validation, and testing phases. The model�s performance is further assessed using performance measures and the visualization of accuracy and loss curves. The results show that the hybrid 1D-CNN-LSTM model works well to accurately find and classify surface discharge tracking defects in switchgear. The model offers precise and dependable fault identification, which has the potential to significantly enhance switchgear functionality. By enabling proactive maintenance and timely intervention, the proposed model contributes to the overall reliability and performance of switchgear in power systems. The findings of this research provide valuable insights for the design and implementation of advanced fault detection systems in switchgear applications. � 2023 Institute of Advanced Engineering and Science. All rights reserved.
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    Publication
    Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods
    (MDPI, 2023)
    Mohammed Alsumaidaee Y.A.
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    Yaw C.T.
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    Koh S.P.
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    Tiong S.K.
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    Chen C.P.
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    Yusaf T.
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    Abdalla A.N.
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    Ali K.
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    Raj A.A.
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    58648412900
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    36560884300
    ;
    22951210700
    ;
    15128307800
    ;
    57883616100
    ;
    23112065900
    ;
    25646071000
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    36130958600
    ;
    57189492851
    The damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains. � 2023 by the authors.
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