# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
###### 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. arcing; 1D-CNN-LSTM; switchgear; faults; deep learning; energy +
Footnote †: journal: Medical Imaging  
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devices, which poses a significant problem in terms of providing reliable insulation to prevent breakdowns [3; 4]. Switchgear can be classified into three types: gas-insulated, oil-insulated, and air-insulated, which are further categorized based on their voltage level into low, medium, and high voltage groups [5; 6]. To ensure continuous and uninterrupted power supply to the end user, regular and thorough monitoring of operational performance and general working condition of switchgear is necessary [7]. In case of any malfunction, quick and efficient troubleshooting along with appropriate countermeasures are required [8]. Switchgear faults and failures can be categorized into several types, one of which is arcing, which is a common issue in switchgear [9; 10]. An arcing fault occurs when a high-voltage plasma discharge creates an electric arc between stage-to-stage or stage-to-neutral conductors, resulting in an arc flash. Several factors that can lead to an arc flash are mechanical failure, overload, or human error such as inadvertently touching the wiring or an electrified busbar [11]. An arcing fault is a high-current, low-impedance fault that occurs when an electrical discharge jumps across a gap in the insulation between conductors. This discharge produces high temperatures that can damage the insulation and the conductors themselves, leading to further electrical problems [12]. Partial discharge is another type of fault that occurs when small electrical discharges or arcs happen between conductors in the insulation material, resulting in localized heating and degradation of the insulation. This fault can eventually lead to the failure of the insulation and cause a short circuit or arcing fault [13]. Arcing faults are dangerous to both people and equipment and can cause big financial losses and halt important operations. Because of this, arc fault mitigation measures have been made to deal with these dangers and increase awareness among workers [14].