# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 1 Introduction  
It is a challenging task for power system operators (PSO) to supply uninterrupted electric power to end-users. Although fault intrusion is beyond human control, it is essentially important to accurately detect, classify and locate the fault location. Fault detection, classification and location finding methods in power transmission systems have been extensively studied [1; 2; 3; 4; 5; 6]. Efforts are under way develop an intelligent protection system that is able to detect, classify and locate faults accurately.  
Advancements in signal processing techniques, artificial intelligence (AI) and machine learning (ML) have aided researchers in adopting a more comprehensive and dedicated approach in studies associated with conventional fault protection strategies. Moreover, two established limitations of online fault detection mechanisms are being dealt with. The first limitation is the difficulty in obtaining the needed data. In order to gain information at different nodes/buses in the grids, intelligent electronic devices (IED) are installed [7; 8]. Adding to this, the development of self-powered, non-intrusive sensors has the ability to build sensor networks for smart online monitoring [9; 10]. The knowledge obtained from the data related to various transmission grid conditions has enabled researchers to develop intelligent fault protection/diagnosis systems. The effects of diversified and complextransmission system topologies can be minimized by using the interspersed sensors for the collection of voltage and current signals. The second limitation is the lack of computational capability and communication. Synchronized global positioning system (GPS) sampling and high-speed broadband communications for IEDs in power grids are proposed in [8]. These technical advancements assure the quick response to faulty scenarios and the effective functioning of online monitoring mechanisms based on sensor networks. The availability of high-performance computing solutions gives provision to the implementation of higher computation complexity methods [7].  
Short circuit faults are more likely to appear in power systems (PS) than the series faults, break in the path of current. Shunt faults result in catastrophes and leave hazardous effects on PS. Short circuit faults can be divided into symmetrical and asymmetrical faults and further classification is presented in Figure 1 for the three-phase system [11].  
An in-depth review of the different techniques used for fault detection, classification, and location estimation has been presented in this paper. This paper also presents a detailed comparison of various fault detection, classification and location methods based on the algorithm used, input, test system, features extracted, complexity level and results. Where, complexity is defined by considering the number of inputs and rules involved in algorithm development throughout this paper as simple, medium and complex. The rest of the paper is organized as follows: feature extraction based on transformation, dimensionality reduction, and the modal transformation is discussed in Section 2. Fault detection methods largely based on feature extraction techniques are presented in Section 3. Fault-type classification methods are presented in Section 4 while a comparison of fault-type classification techniques is presented in Section 5. Section 6 describes the future prospects of fault-type classification. Fault location finding approaches are reviewed in Section 7 and a comparison of fault location finding methods is outlined in Section 8. Future trends in fault location finding techniques are proposed in Section 9. Strengths and weaknesses of notable emerging computational intelligence methods are presented in Section 10. Finally, conclusions are drawn in Section 11.