Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

Ishak, Sanuri; Yaw, Chong Tak; Koh, S. P.; Tiong, Sieh Kiong; Chen, Chai Phing; Yusaf, Talal

doi:10.3390/en14196279

Cited by 10 publications

(4 citation statements)

References 64 publications

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“…While deep CNNs were initially used for image classification tasks, they have since been used for video analysis and recognition. However, the use of 1D sequence data for classification and prediction is relatively new [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. The corona and non-corona categories can be seen as a sequential modeling effort, making 1D CNNs a suitable choice.…”

Section: Theoretical Study and Methodsmentioning

confidence: 99%

“…To maintain switchgear safety and ensure a stable power supply, it is crucial to detect and closely monitor corona discharge activities [ 10 ]. A corona discharge in switchgear results in the release of three types of energy: electromagnetic radiation, ultrasonic waves, and gases such as ozone and Nitrogen Oxides (NOx) [ 11 ]. Electromagnetic and ultrasonic techniques have been utilized to detect and monitor corona discharge activity in medium-voltage switchgear with reduced interference and improved efficiency [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…A corona discharge in switchgear results in the release of three types of energy: electromagnetic radiation, ultrasonic waves, and gases such as ozone and Nitrogen Oxides (NOx) [ 11 ]. Electromagnetic and ultrasonic techniques have been utilized to detect and monitor corona discharge activity in medium-voltage switchgear with reduced interference and improved efficiency [ 11 ]. In particular, electricity-based methods are commonly employed to measure discharge activities.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

Alsumaidaee

Yaw

Koh

et al. 2023

Sensors

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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.

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Section: Theoretical Study and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

Alsumaidaee

Yaw

Koh

et al. 2023

Sensors

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“…Failures in switchgear can be broadly classified into two types: mechanical faults and electrical faults [3]. Wear and tear, corrosion, misalignment, and component failure all fall under the category of mechanical faults [4].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Medium Voltage Switchgear Using a Deep Learning Hybrid 1D-CNN-LSTM Model

Alsumaidaee,

Paw,

Yaw

et al. 2023

IEEE Access

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Medium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults in switchgear include arcing, tracking, corona, normal cases, and mechanical faults. Accurate detection of these faults is essential for maintaining the safety of MV switchgear. In this paper, we propose a novel approach for fault detection using a hybrid model (1D-CNN-LSTM) in both the time domain (TD) and frequency domain (FD). The proposed approach involves gathering a dataset of switchgear operation data and pre-processing it to prepare it for training. The hybrid model is then trained on this dataset, and its performance is evaluated in the testing phase. The results of the testing phase demonstrate the effectiveness of the hybrid model in detecting faults. The model achieved 100% accuracy in both the time and frequency domains for classifying faults in Switchgear, including tracking, normal cases, and mechanical faults. Additionally, the model achieved 98.4% accuracy in detecting corona faults in the TD. The hybrid model proposed in this study provides an effective and efficient approach for fault detection in MV switchgear. By learning spatial and temporal features simultaneously, this model can accurately classify faults in both the TD and FD. This approach has significant potential to improve the safety of MV switchgear as well as other industrial applications.

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Condition-based monitoring techniques and algorithms in 3d printing and additive manufacturing: a state-of-the-art review

Siddiqui,

Tabassum

2024

Prog Addit Manuf

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Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

Cited by 10 publications

References 64 publications

Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

Fault Detection for Medium Voltage Switchgear Using a Deep Learning Hybrid 1D-CNN-LSTM Model

Condition-based monitoring techniques and algorithms in 3d printing and additive manufacturing: a state-of-the-art review

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