# Detection of Arc Faults in Transformer Windings via Transient Signal Analysis
## 1 Introduction  
Transformer winding faults caused by lightning strikes, partial discharges, and switching events pose a significant challenge for the continuous transmission of energy to consumers. Initially, these faults manifest as electrical arcs, but they can evolve into rigid short circuits, leading to damage to the transformer windings. If not detected and addressed in the early stages, these faults may result in complete transformer failure, causing long-term power outages. A review of studies on transformer fault detection reveals that very few have focused on the early detection of arc faults; most research is concentrated on rigid short circuit faults resulting from direct conductor contacts. However, prior to a rigid short circuit, small currents flow in an arc, and the temporary effects of a minor arc may not exhibit the symptoms of a significant arcing fault. At this stage, techniques such as Buchholz relays and differential protection prove insufficient. Recent literature indicates that studies on the detection of transformer winding faults have primarily concentrated on methods based on frequency response analysis (FRA) [1; 2; 3; 4] and dissolved gas analysis (DGA) [5; 6; 7; 8]. FRA can be of two types: low-voltage impulse (LVI) or sweep frequency response analysis (SFRA). In the SFRA method, the frequency response of the transformer is obtained through connections made at the transformer's terminal using the SFRA device. By analyzing changes in this frequency response through various techniques, it is possible to determine whether there is a fault condition between the windings of the transformer. Most studies on fault detection have been conducted when the transformer is offline, meaning it has been removed from the circuit; however, the method has also been applied for online fault detection [9]. This approach requires the connection of a very expensive SFRA device for each transformer and is primarily used to confirm faults in transformers where a faultis suspected. In their studies, [PERSON] and [PERSON] utilized FRA and aimed to achieve the early detection of winding faults in transformer windings by employing an ANN based on frequency response (FR) in the high-impedance stage [10]. To verify the adequacy of their proposed approach, they used a winding model as a test object. Various low and high-impedance short-circuit faults were tested in the winding model, allowing for the measurement of the frequency response of the winding in both intact and faulted states using the LVI test. A multi-layer perceptron (MLP) neural network was employed to map the frequency response to the precise location of each fault. However, these studies did not address the arc fault condition in its initial state, and the method generally requires the transformer to be offline and tested under laboratory conditions. In the study presented by [PERSON] et al., frequency response components obtained through FRA were utilized for fault type detection in distribution transformer windings. Heat maps based on appropriate statistical indices were generated to illustrate the changes in frequency responses related to each fault type, fault location, and fault extent along the windings [11]. Analysis of these data revealed that the generalized regression neural network method is the most effective solution for classifying transformer winding faults. However, this method also necessitates that the transformer be disconnected from the grid for FRA. The traditional method used to detect transformer winding faults is dissolved gas analysis (DGA). The two main electrical activities that occur in a transformer due to electrical stress are arcing and corona, also known as partial discharge [12]. Partial discharge in oil (PD), which applies to oil-type transformers, is a low-energy activity (about 700 \({}^{\circ}\)C) caused by the suspension of many microbubbles in the insulating oil. These individual electrical discharges last less than 1 ms.