## 1 Introduction  
An important and critical element of the power network is the transmission line. It transfers power in a bulk quantity from generation plants to grid substations (GSS), which are located near the load centers. These power lines cross the geographical areas of different climatic conditions [1]. Hence, there is the maximum probability of fault occurrence on transmission lines in comparison to the rest of the elements of the power system network. Fault incident on the transmission line causes long-term power outages. This may also cause harmful effects on the connected consumer equipment [2]. Hence, protective relays must detect fault events immediately, isolate the fault section of the line, and perform classification to identify the nature of the fault. Articles have been reported on many algorithms, which focused on detecting fault conditions incident on power transmission lines and the identification of the fault phase, as well as the nature of the fault. However, many of these algorithms are complex in nature and involve the measurement of data on both terminals of the line, resulting in the additional requirement of the Global Positioning System (GPS) and medium for communication [3; 4; 5; 6]. In [7], the authors presented a method for the identification and classification of faults incident on transmission lines using the convolutional sparse auto-encoder (CSAE). This method automatically identifies the features from the dataset, which are obtained with the help of the signals of the voltage and current and utilized for fault recognition. The mapping of features is achieved with the help of local translation invariance for the channel signal segments over the period of half a cycle. An algorithm using voltage and current based features, extracted using the Stockwell transform (S-transform) for identification and classifying faults on the power system, is available from [8]. A scheme using wavelet packet transform (WPT) for the estimation of the fault section and phase on a multi-terminal transmission line was discussed in [9]. An algorithm depending on hybrid features evaluated using the Wigner distribution function (WDF) and the alienation coefficient was applied for the protection of the power transmission line (PTL) [10] of a utility network with solar energy penetration [11] and a hybrid grid with wind and solar energy penetration [12]. This method has the merits of the fast recognition of faults for a time period of less than a quarter cycle. A method to estimate fault direction using the positive sequence voltage and current was presented in [13]. In [14], the authors presented a digital relaying scheme depending on the entropy principle, as well as the fast discrete orthogonal S-transform (FDOST) for the identification, classification, and localization of faults on the PTL. This algorithm seems to be independent of the fault conditions, the incidence angle of the fault, and the resistance during the fault event and quickly and accurately detects the type of fault. An algorithm using current based feature extraction by the discrete WT and rule dependent decision tree for identifying and classifying faults on the transmission line was presented in [15; 16]. However, it is affected by the existence of noise. Approaches based on the features of the voltage and current signals computed using the Stockwell transform (ST) and Hilbert transform (HT) for the estimation of fault conditions for a distribution feeder were reported in [17; 18; 19]. Therefore, based on the review of the literature discussed in the above paragraph, it was established that the development of techniques that are less complex eliminates the use of communication channels, and the GPS system is necessary to provide the protection to the transmission lines.