# Detection of Arc Faults in Transformer Windings via Transient Signal Analysis
## 1 Introduction
Partial discharge in the insulating paper is initiated by small holes that form in the paper windings adjacent to the conductor. An arc is a long-lasting electrical discharge caused by a high-energy discharge in the temperature range of 800-1200 \({}^{\circ}\)C [13]. During faults, oil and paper separation occurs due to electrical and thermal stresses. This decomposition releases gasses that reduce the heat dissipation capacity and dielectric strength of the transformer oil [14]. Partial discharge by-products include hydrogen (H\({}_{2}\)), traces of methane (CH\({}_{4}\)), and traces of acetylene (C\({}_{2}\)H\({}_{2}\)). The partial discharge reaction also produces other by-products, such as moisture and discharged particles (ions and electrons). Both arc and partial discharge generate H\({}_{2}\), CH\({}_{4}\), and C\({}_{2}\)H\({}_{2}\) gasses. The combination of large amounts of C\({}_{2}\)H\({}_{2}\) and H\({}_{2}\) indicates that an arc reaction occurs in the oil [15; 16]. Although the DGA of transformer oil is one of the most effective tools for monitoring the condition of power transformers, and there are many interpretation techniques for DGA results, all of these techniques rely on personnel experience rather than standard mathematical formulas. Consequently, a significant number of DGA results fall outside the recommended codes of existing methods and cannot be diagnosed by these techniques. Given the uncertainties in diagnosing reactions in oil, the inability to interpret gas combinations with certainty continues to be a challenge in condition monitoring for power transformers. Furthermore, gas content measurement can only be performed in oil-type transformers; therefore, DGA-based methods cannot be applied to dry-type transformers. In addition to the previously mentioned studies that have gained general validity, interest in detecting transformer faults has increased in recent years. Important studies based on various techniques on this subject have begun to emerge in the current literature. In the study conducted by [PERSON] et al., frequency response components obtained via FRA were used for fault type detection in distribution transformer windings. Heat maps, based on appropriate statistical indices, were created to illustrate changes in frequency responses relating to each fault type, fault location, and fault extent along the windings. According to the results obtained using ANN, it was stated that the generalized regression neural network method was identified as the most effective solution for classifying transformer winding faults [11]. In a study conducted by [PERSON] et al., which is among the limited research on arcs developing in transformer windings, a model was created to enable the direct integration of the Schavemaker model. This model calculates the finite element circuit-coupled and time-varying arc conductivity for arc faults occurring between transformer windings. Using the simulation model of a power transformer, flux distribution, short-circuit circulating current, and terminal currents were determined, with discrete wavelet transforms (DWT) used to extract the characteristics of the terminal current. This method was evaluated to provide distinctive features for detecting transformer faults and could serve as an effective tool for enhancing protection performance [17]. [PERSON] et al. proposed a new indicator based on reverberation time, widely used in acoustics, to determine transformer faults. This proposed parameter is based on time-domain calculations and offers reliable detection of short circuit faults occurring in transformer insulation, as well as the accurate estimation of fault magnitudes. An important advantage of this approach is that it allows for the use of low-cost measurement and signal processing devices, with low dependence on the frequency range of the measured signal [18]. [PERSON] et al.