# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 1 Introduction
Using the features of the three-phase current and voltage phase angle information obtained from this simulation, fault type detection was performed with SVM [31]. In [PERSON] et al., fault detection was performed using the sparse representation method, but this has the disadvantage of limited processing power for big data [32]. [PERSON] et al. used random forest (RF) for fault detection, but this has the disadvantage of being quite slow [33]. [PERSON] et al. used K-Nearest Neighbors for fault detection. However, this method has disadvantages such as being sensitive to noise and outliers [34]. [PERSON] et al. built a two-bus, 200 km, and 400 kV transmission line model in the laboratory. Using the LabVIEW program, data from this actual system were extracted and fault type detection was performed with a wavelet transform-based multiresolution analysis (MRA) filter [35]. [PERSON] and [PERSON]'s 500 kV, 350 km, two-bus transmission line model was created in the Matlab/Simulink simulation program. The three-phase current data were analyzed using a morphological edge detection (MED) filter to extract distinguishing features, and fault type detection was performed [36]. In [PERSON] et al., fault detection was performed using the extreme learning machine (ELM). However, this method has the disadvantage of not being able to train large data quickly and efficiently [15]. [PERSON] et al. performed fault detection using convolutional neural networks (CNN). However, training CNNs takes a long time, especially with large data sets [37]. In [PERSON] et al., the long short-term memory (LSTM) algorithm was proposed to detect fault types and locations. However, this method also has the disadvantage that it is prone to overfitting [38]. [PERSON] et al. performed fault detection using deep graph learning methods. However, this type of method is not robust to noise in the data [39]. In [PERSON] et al., the MCECA-CloFormer method was proposed as a novel approach for single-phase ground fault detection in distribution networks. The method utilizes zero-sequence current signals as the primary input data, representing a significant advancement in the field [40]. [PERSON] et al. proposed a method for fault detection that supports phasor measurement unit (PMU)-based fault detection and localization [41]. [PERSON] et al. developed a dynamic Bayesian network (DBN)-based model for the normal operation of cables and the detection of different types of faults. In this study, the fault location is also determined with high accuracy [42]. [PERSON] et al. endeavored to detect a single-phase short circuit fault in a distribution network with distributed generators. The magnitude and phase angle differences of the current vectors were utilized to locate the single-phase fault [43]. In the literature, although short-circuit faults are very common, there are few studies that investigate the detection of high-impedance faults and sensor-induced noise. In this paper, we describe a new algorithm to detect the fault type and location in distribution networks using random vector functional link networks (RVFLN). The proposed algorithm uses three-phase current and three-phase voltage measurements to generate a feature vector for the fault type and location. Of these 14 feature vectors, six of them are newly developed for the detection of high-impedance faults. In this way, a 10% success increase is achieved in the detection of high-impedance faults. In this study, we first build a single-bus transmission system in the PSCAD simulation program and use the simulation data to train the model for fault type and location detection. In this single-bus transmission system, the fault type and location are detected with high accuracy. We then analyze the performance of the model in multi-bus systems that we encounter in real-life distribution networks. For this purpose, the IEEE 39-bus system is modeled using the Real-Time Digital Simulator (RTDS) for fault type and location detection. Data are taken from faults of different types and impedances generated in the model.