# Fault Classification and Localization Scheme for Power Distribution Network
## 1 Introduction  
The importance of the electrical power system's reliability and security can never be over-emphasized. Electricity supply is at the center of the economic development of any state. The traditional electricity topology is organized into different segments, which include the generation, transmission, and distribution systems. The distribution segment is the interlink between the electricity generation cycle and the load supply demand. Thus, special care is required for distribution systems to secure a reliable supply of electricity to the required consumers for a convenient duration. Power distribution networks are prone to external faults as a result of the overhead technical convenient design philosophy. These faults may result in serious catastrophic consequences, such as prolonged power outages, critical loads interference and economic stagnation. For instance, in 2003, a major blackout ensued in New York city, affecting the social being of the citizens in the area because of the protection scheme failure [1]. Thus, it is imperative to develop protection schemes capable of interrupting the potential impact of faults in power systems. Power system protection can be defined as the art of designing a monitoring system that detects any disturbance that may affect the supply of power to the required load demand. Fault occurrences take place when there is an insulation failure between the phase conductor and the ground and between the phase conductors. When the insulation fails, the current magnitude at the point of the fault increases beyond the nominal current limit rapidly. The fault types are classified as (a) single line-to-ground (SLG), (b) line-to-line (LL), (c) double line-to-ground (LLG), and (d) triple line fault (LLL) [2]. In modern power distribution protection systems, intelligent electronic devices (IEDs) are utilized to monitor any disturbances that may occur in the system. IEDs mostly use discrete Fourier transforms (DFT) for signal interpretation. However, the main drawback of DFTs for power system applications is the time resolution due to a high-frequency resolution being required [3]. In recent past years, many researchers have investigated fault classification and estimation schemes for power distribution systems. The design of these schemes follows thearchitecture of (i) signal measurement, (ii) signal decomposition and analysis, (iii), feature extraction and selection, (iv) fault classification, and (v) fault location. A hybrid protection scheme based on wavelet transform (WT) and probabilistic neural network (PNN) was proposed for fault classification in a power system [4]. The scheme utilized the WT technique for signal processing and feature extraction. The extracted features from WT are fed into the PNN algorithm for fault classification. The studies in [5; 6] proposed protection schemes based on the combination of WT and fuzzy logic (FL). In these studies, WT was employed for signal decomposition, and FL was used for fault classification. In these schemes, a simple computation process is used; however, the classification error reported is largely due to inconsistent simulation conditions. In [7], a technique based on WT for fault detection in a transmission line was proposed. The techniques used Daubechies 4 (db4) mother wavelet for signal tracking and analysis. In [8], a technique based on discrete wavelet transform (DWT), an artificial neural network (ANN), and an extreme learning machine (ELM) for distribution fault detection were proposed. The proposed technique used DWT for signal processing and feature extraction, the ANN algorithm was used to classify different types of faults, and ELM was used for fault location. The scheme produced good results with minimal time delay. [PERSON] et al. proposed a deep learning fault detection technique based on the Hilbert-Huang transform (HHT) and a convolutional neural network (CNN) [9]. The technique uses HHT to extract energy features from the fault signal, and CNN is employed for fault classification.