###### Abstract  
Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use. neural network; wavelet transform; signal processing; backpropagation; multilayer perceptron; fault detection +
Footnote †: journal: Applied System  
18 November 2021  
Footnote †: journal: Applied System  
1 Department of Electrical Engineering, Emalahleni Campus, Tshwane University of Technology, Emalahleni 1034, South Africa; [EMAIL_ADDRESS] (D.O. ); [EMAIL_ADDRESS] (D.H.); [EMAIL_ADDRESS] (A.N.) Footnote †: journal: Applied System  
1 Department of Electrical Engineering, Emalahleni Campus, Tshwane University of Technology, Emalahleni 1034, South Africa; [EMAIL_ADDRESS] (D.O. ); [EMAIL_ADDRESS] (D.H.  
conditions and disconnect the faulted area from the rest of the network. At present, there are two types of protection schemes in use: unit and non-unit protection. Unit protection uses information gathered from both ends of the transmission line, resulting in costly communication links at either end. These communication links cannot be guaranteed to maintain contact at all times, as every communication network is influenced by external factors [6]. Non-unit protection schemes only use information gathered from a single end of the transmission line; thus, no communication network is needed [7; 8]. However, these systems cannot detect faults in the overlapping areas of protection on the transmission line [6]. According to [PERSON], currently, the solution in place to overcome this is backup protection, resulting in an additional cost for the scheme in the form of extra equipment [1]. This study focuses on a different method of non-unit protection. In this study, the boundary protection of transmission lines using wavelet transforms and a neural network was investigated. The proposed technique uses the transient signal generated during fault conditions to identify faults [7; 9; 10]. The fault signal is broken down into the frequency components using a wavelet transform, and the resultant signals are used by a neural network to classify the fault location according to the faulted line [11; 12; 13]. With traditional methods of fault detection, distortions in relations between voltage and current phasors may cause false tripping [11; 14]. By using a neural network, more accurate fault identification and classification are achieved than with systems currently in use [12; 14]. A neural network was found to be the best form of artificial intelligence for this application.