# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
###### Abstract
To quickly and effectively suppress arcs, various protection strategies and the effects of arc short-circuit are discussed in these studies [15; 16]. The energy loss caused by an arc discharge is affected by the arc voltage, current, and duration. Thus, there are several approaches to minimize arc duration or short-circuit duration and limit the energy of the arc [17; 18]. One common technique is moving the arc from its original location by grounding, while another method is to activate the primary switch and accelerate protection using sensors other than traditional current transformers. These methods can decrease the duration of the arc and reduce its incident energy, as well as the magnitude of the arc current in some cases [19]. There are two main types of arcing faults [20]. The first type is a \"series arc fault\" which happens when a single wire is damaged to the point that it cannot carry current. Arcing then occurs in the gaps between the conductors and spreads to the insulator. The second type is a \"parallel arc fault\" which occurs when current passes through defective insulation and shorts out as it travels between conductors. This type of fault can be difficult to detect by circuit breakers since it is a weak circuit fault that creates arcs as the leakage current passes through the insulating components [21]. Typically, the conventional sensor module in the switchgear provides the most recent data on the current. Arc faults can be addressed by using either a differentiated protection system or regular overcurrent protection [22]. The time required for contemporary relays and circuit breakers to detect and interrupt an arc fault is normally at least 50 milliseconds (ms), which includes the trip relay contact closing and the switch opening. Depending on the relay types and circuit breaker technology, the automatic trip time can reach 50 ms or longer. This delay can be due to the need to blow off lids or doors (in gas-insulated switchgear) with sufficient force to ensure that the arc fault is extinguished before the pressure peak [23; 24]. Additionally, it can be caused by the operator in the switch room releasing hot gas, or by shortening the time for the circuit breaker (via loop sensor) to trip when an abnormal light is detected in the switchgear assembly, thereby accelerating the protective logic's response time. There are several ways to recognize and monitor these faults in switchgear and control gear. Diagnostic testing, visual inspection, and condition monitoring are some of the most effective methods [25; 26; 27]. Diagnostic testing is an important technique that is used to evaluate the condition of switchgear and control gear. It involves subjecting the equipment to various tests including insulation resistance testing, dielectric testing, and partial discharge testing. These tests can identify any weak pointsin the insulation or other components of the equipment that may be susceptible to arcing or partial discharge [28]. Visual inspection is another important technique that can help to recognize arcing faults and partial discharge in switchgear and control gear. Inspectors should look for signs of damage, wear, or overheating, such as discoloration or burn marks on the equipment, as well as debris or other contaminants that may have accumulated inside the equipment [27]. Condition monitoring is a more advanced technique that involves monitoring the equipment's performance in real-time using sensors and other monitoring devices. This technique is especially useful for detecting early signs of arcing and partial discharge that may be missed by other inspection methods. For example, a monitoring system can detect increases in temperature, changes in electrical signals, or other anomalies that may indicate a developing fault [5]. In recent years, deep learning has made significant progress and has been extensively applied in various fields, particularly in images [29] and natural language processing [30], as well as in the medical [31], industrial [32], and energy sectors [33; 34].