# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 1 Introduction  
In distribution networks, it is crucial to ensure the safe and high-quality delivery of electricity to the end user [1, 2]. Today, distribution grids have become quite complex [3, 4]. The main reasons for this complexity are the inclusion of renewable energy sources, electric vehicles, and electric energy storage systems in distribution grids [5]. Due to this complex network structure, distribution networks are considered highly intensive networks [6, 7]. According to previous studies, 80% of the failures occurring in power transmission and distribution systems occur in distribution networks [8, 9]. Failures in distribution networks also damage national economies. For example, according to the report provided by the European Network of Transmission System Operators for Electricity (ENTSO-E), in 2020, a total of 1589 faults occurred in the Baltic and Scandinavian countries, resulting in a loss of 2257.2 MWh in the energy supply. Moreover, 55.125% of the faults in the electricity systems of the Baltic and Scandinavian countries are caused by faults in distribution networks [10]. Therefore, fault location and fault type analysis are critical for the safe and stable operation of distribution networks [11, 12]. Such interruptions are caused by various types of short-circuit faults in distribution networks. These faults consist of double-phase ground faults(2 ph-g), single-phase ground faults (1 ph-g), and phase phase (2 ph) faults. In distribution networks, 70% of these faults are single-phase ground faults, 15% are two-phase faults, 10% are two-phase ground faults, and 5% are three-phase (3 ph) faults [13; 14; 15]. These faults occurring in distribution networks are detected by conventional protection devices such as protection relays and overcurrent relays. However, high-impedance faults (HIFs) may not be detected by conventional protection devices due to their low current amplitude [16; 17]. In particular, 17.5% of HIFs cannot be detected by conventional protection methods [18]. High-impedance faults (HIFs) represent 5 to 20% of the faults occurring in distribution networks (DNs) [19]. The most prominent characteristic of HIFs is that they are subjected to an electric shock, creating arcing, which poses a great risk to humans and animals. HIFs are identified as the second-largest cause of fatalities related to distribution networks in Brazil. Specifically, in 2013 and 2014, HIFs resulted in 30 and 53 fatalities in Brazil, respectively [12; 20]. HIFs are a problem that usually exists in power transmission and distribution systems and also cannot be detected by conventional protection mechanisms. Hence, an intelligent mechanism is required to learn the patterns of different systems belonging to different distribution networks [21; 22]. There are many studies in the literature on fault type and location detection using traditional machine learning classification methods [23; 24; 25]. In [PERSON] et al., fault detection was performed using a decision tree, but the decision tree method has the disadvantage of overfitting [26]. [PERSON] et al. performed fault detection using artificial neural networks (ANN), but they had a slow learning rate [27]. [PERSON] and [PERSON] performed fault type detection using an ANN algorithm for three-phase current and voltage data obtained from a 400 kV, 2-bus model [28]. [PERSON] and [PERSON] created a 400 kV, 200 km, two-bus, two-generator, and two-load model. Using three-phase current and voltage magnitude and three-phase current angle data from this model, the fault type and fault location were determined via the fuzzy logic method [29]. [PERSON] et al. used support vector machines (SVMs) for fault detection, but the SVM method has the disadvantage of requiring a large amount of memory [30]. [PERSON] et al. created a 400 kV, 300 km, two-bus, two-generator, and two-phasor-measurement-unit (PMU) model in the Matlab/Simulink simulation program.