# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 1 Introduction  
An electric power system includes three parts: generation, transmission, and distribution. The electric power produced at the generation stations is transmitted over long distance with high-voltage transmission lines. The voltage level is stepped down at distribution substations. Low-voltagedistribution lines deliver the required power to the end users. Due to the growing electricity demand, distribution lines are getting more crowded time after time. As the number of lines and their length increases, the chance for occurrence of fault also increases. Faults in the distribution network can occur as a result of lightning, short circuits, malfunctioning devices, human error, overloading, aging, and so on. These faults cause mechanical damages to be repaired before the line is returned to service. Short- to long-term power outage can happen before the system is restored depending on the nature of the fault. The recovery can be accelerated if the position of the fault is known or can be estimated with reasonable accuracy. In order to have a reliable power system, a fast fault detecting, isolating, and location mechanism is crucial. The fault location methods can be categorized as impedance, traveling wave, and knowledge-based methods based on the behaviors inherent in the distribution network [1; 2]. The impedance-based fault location method uses measured current and voltage signals at two one-end or two-ends of the line and calculates the impedance to estimate the fault location [3]. Depending on the utilized input signals and nature of the transmission line in consideration, varieties of the impedance-based fault location method can be used. This method is simple and economical for implementation. The major drawbacks of this technique are the necessity of multiple estimation and hectic iterative processes [4]. The second category of the fault location method is based on traveling wave. The traveling wave is a high-frequency electromagnetic impulse, due to unexpected change of current at the faulted point on a distribution line, which propagates away from the fault in both directions. With the aid of signal processing tools, the captured signals are filtered and analyzed and then it is used as a feature for detection and location of the fault. To find the exact location, it is essential to measure the phase, value, polarity, and time delay of the incoming wave [5]. Fault location based on traveling wave is categorized as type A, B, and C. For type A and B, it uses single or double ended recorded traveling waves during fault and is measured online, whereas the type C technique is measured offline using a manually injected traveling wave signal to locate the fault [6]. Although these methods are more accurate than the impedance method for very long overhead transmission lines, they are complex and costly as they require high sampling frequency [7]. Moreover, their accuracy is in the range of 100-500 m, which is too long for applications in a distribution network that consists of very short transmission lines. The third category of fault location techniques follows the knowledge-based method. Much study has recently been carried out on fault location techniques in transmission and distribution networks using knowledge-based methods such as supporting vector machines (SVM) and artificial neural networks (ANNs) [1; 2]. In Reference [8], the wavelet transform is combined with different machine learning tools to locate the fault in the distribution grid. Extreme learning machines (ELMs) are widely used in diagnosing faults in transmission lines [9; 10; 11]. They are also used for fault detecting in power transformers [12; 13; 14] and generators [15], and owing to their quicker learning speed and increased generalization efficiency, ELM is reported to be more accurate than the other techniques [6]. Due to their poor accuracy, impedance-based and traveling wave fault location methods have limited application for distribution networks.