# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 2 Modelling of 330 kV Transmission Line  
Machine learning needs many datasets for practical training, and those datasets were obtained from a model of a 330 kV, 500 km transmission line network as shown in Figure 1 below. The parameters from Tables 1 and 2 were used to create the model in Simulink as in Figure 2. This model generated the fault data of a single line-to-ground, double line-to-ground and three-phase-to-ground fault. These data were used to train machine learning for fault classification. They were also applied to validate the data for accuracy, root mean square error (RMSE) and precision of result. Figure 1 represents the three-phase, 330 kV transmission line model developed and implemented in this article. It consists of a Nigerian 330 kV transmission line which cut across 500 km and was modelled using Matlab/Simulink. The ground resistance used was 0.01 \(\Omega\) based on the IEEE recommendation for ground resistance, which is ideally in the 0-50 \(\Omega\) range [33]. In addition, a minimum fault line voltage of 0.001 V (minimum standard value) and an incipient fault angle (0 to \(-30^{\circ}\)) were used to derive the maximum arc resistance value. Small ground fault resistance was chosen to detect a transient fault because a higher resistance value would lead to excess voltage and current, so the system might not classify minor faults. Therefore, the higher the fault resistance, the lower the fault detection. A three-phase fault simulator was used to simulate the fault at different locations on the transmission line for proper classification. Figure 1: A 330 kV three-phase, 500 km transmission line model. Table 1 shows the model parameters where \(R_{1}\) and \(R_{2}\) are positive and negative sequence resistances of phases 1 and 2, respectively. \(L_{1}\), \(L_{2}\) and \(L_{3}\) represent the positive and negative sequence inductances of phases 1, 2 and 3, respectively, whereas \(C_{1}\), \(C_{2}\) and \(C_{3}\) represent the positive and negative sequence voltages of phases 1, 2 and 3, respectively. Finally, \(R_{0}\), \(C_{0}\) and \(L_{0}\) represent the zero resistance, capacitance, and inductance sequence, respectively. Tables 1 and 2 represent input data for the modelling of the 330 kV 500 km transmission line. Simulations were carried out by inducing a fault into the line at 300 km. The parameters were carefully selected based on the standard of the International Electromechnical Commission (IEC 60909) [34]. The fault voltage and current data were generated from the model in a different scenario, and 12 fault conditions were considered: are a-g, b-g, c-g, a-b, b-c, a-c, a-b-g, b-c-g, a-c-g, a-b-c-g and no-fault, as seen in Table 3, where a = fault at phase A; b = fault at phase B; c = fault at phase C, and g is the ground fault. The binary representation showed the fault and no-fault conditions representing 1 and 0, respectively. It indicated the fault number assigned to each fault condition. \begin{table}
\begin{tabular}{l l l l} \hline
**Sequence** & **Parameter** & **Value** & **Unit** \\ \hline Positive and negative sequence resistance & \(R_{1}\), \(R_{2}\) & 0.01273 & \(\Omega\)/km \\ Zero sequence resistance & \(R_{0}\) & 0.3864 & \(\Omega\)/km \\ Positive and negative sequence inductance & \(L_{1}\), \(L_{2}\), \(L_{3}\) & 0.9337 \(\times\) 10\({}^{-3}\) & H/km \\ Zero sequence inductance & \(L_{0}\) & 4.1264 \(\times\) 10\({}^{-3}\) & H/km \\ Positive and negative sequence capacitance & \(C_{1}\), \(C_{2}\), \(C_{3}\) & 12.74 \(\times\) 10\({}^{-9}\) & F/km \\ Zero sequence capacitance & \(C_{0}\) & 7.751 \(\times\) 10\({}^{-9}\) & F/km \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters of 330 kV, 500 km transmission line.