## 4 Result and Discussion
### Decision Tree Method Sample Selection  
This subsection explores the performance of the decision tree classification method in detecting the four types of faults during operation. All extracted features are implemented in MATLAB, where a set of 840 samples contained four types of fault are mixed randomly and trained into the classification system. Several parameters in the decision tree namely the maximum number of splits and predictor selection are varied to find the best classifier to be used in the study based on its percentage accuracy. Furthermore, the confusion matrix algorithm is adopted in the decision tree to present the performance of classification  
Figure 15: Fault signature for voltage phase sinusoidal waveform.  
accuracy of the fault causes. Table 5 shows a set of 10 samples containing five variables as the input data and those four types of fault were assigned as 1, 2, 3, and 4, respectively. Table 6 defines the actual output variables which later to be set as an array in the system to ease the computation process.  
Figure 16 shows the decision tree diagram with the average number of splits of five which is not cross validated at the early stage. The root node of the features is T1090 whereby if the duration is greater or equal to 0.0857 s, then the fault is defined as tree encroachment. Otherwise, if it is less than 0.0857 s, it will create another internal node. If the voltage dip is equal to or greater than 19.52%, the fault cause is termed as lightning, or else, it creates the other internal node. Next, if the wavelet energy is less than 0.113, the leaf node is concluded as the crane encroachment. Otherwise, the decision tree will look into T50 and T20. Based on these two features, the faults can be categorized as a crane or insulator degrading. If the T50 is less than 0.0599 s, and T20 is greater than or equal to 0.0716, the leaf node is defined as insulator degrading. Otherwise, if T50 is greater than or equal to 0.0599 s and T20 less than 0.0716, the fault causes are concluded as the crane encroachment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Label** & **Description** & **Unit** \\ \hline \multirow{4}{*}{Input} & Var 1 & Fault duration from 10\% to 90\% increase (T10/90) & s \\  & Var2 & Fault duration from at 20\% increase (T20) & s \\  & Var 3 & Fault duration from at 20\% increase (T50) & s \\  & Var 4 & Voltage drop (Vd) & \% \\  & Var 5 & Wavelet energy (Ener) & - \\ \hline \multirow{4}{*}{Categorical variable / Output} & 1 & Lightning & - \\  & 2 & Insulator degrading & - \\ \cline{1-1}  & 3 & Tree encroachment & - \\ \cline{1-1}  & 4 & Crane encroachment & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Description for input and target output label.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Input**} & \multicolumn{2}{c}{**Categorical**} \\ \cline{2-6}  & **Var 1** & **Var 2** & **Var 3** & **Var 4** & **Var 5** & **Variable/Output** \\ \hline
1 & 0.0142 & 0.0720 & 0.0596 & 7.7798 & 0.1695 & 2 \\
2 & 0.0136 & 0.0826 & 0.0768 & 39.3691 & 0.9252 & 1 \\
3 & 0.0150 & 0.0708 & 0.0580 & 9.1606 & 0.2659 & 2 \\
4 & 0.0138 & 0.0712 & 0.0584 & 6.7943 & 0.1474 In addition, the decision tree is cross-validated using 10 fold and iterated 10 times with a different maximum number of splits set up. In this study, the maximum number of splits are evaluated between 1 to 10 with the accuracy of each splits is then evaluated. Table 7 shows the least percentage accurate obtained when the number of splits is set at 1 which is 52.048% while the highest percentage accuracy is obtained when the number of splits is set at 3, 4, 8, and 9 which is translated in 99.829%. This defines that the minimum of split with the highest accuracy, which is 3, was indicated as an optimum number of split chosen. Therefore, the decision tree algorithm with a number of splits of 3 is considered and implemented in the next process.