# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Fault Type** & **Classes** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline A-G & 1 & 1.00 & 0.70 & 0.83 & 6913 \\ B-C-G & 2 & 0.39 & 1.00 & 0.56 & 7048 \\ A-B-C-G & 3 & 1.00 & 0.72 & 0.83 & 7086 \\ No fault & 0 & 0 & 0 & 0 & 0 \\  & Micro Avg & 0.61 & 0.81 & 0.69 & 21,047 \\  & Macro Avg & 0.80 & 0.81 & 0.74 & 21,047 \\  & Weighted Avg & 0.80 & 0.81 & 0.74 & 21,047 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fault classification report. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & 0 & 0 & 0 & 6955 & 0 \\  & 1 & 0 & 4862 & 2051 & 0 \\  & 2 & 0 & 0 & 7048 & 0 \\  & 3 & 0 & 0 & 2013 & 5073 \\  & & 0 & 1 & 2 & 3 \\  & & & & & Predicted Class \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix for the fault classification. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Technique Used** & **Input Parameter** & **Fault Types** & **Data Size** & **\% Accuracy** & **Strength** & **Weakness** \\ \hline WT, CNN [20] & vibration signal & 400 different fault condition & 2400 & 97.78\% & 8 s to execute and easy to use & The WT Packet is not \\ ANN [37] & Three-phase voltage and current waveform & 10 different fault condition & 7920 & 78.1\% & Easy to use and implement, Repro-gramming is not \\  & Three-phase voltage and current waveform & 10 different condition & 7920 & 78.1\% & Easy to use and implement, Repro-gramming is not needed & It needs a system with a high processor, Longer training \\ Proposed Proposed & Voltage and current signal & Single-phase fault, double phase fault, three-phase fault and no-fault & 93,340 & 99.54\% & Higher accuracy, high-speed and performance low training & It needs a system with a high processor, Longer training & It needs a system with a high processor, Longer training & It needs a system with a high-performance system \\ BPNN [11] & Voltage and current & AG, BG, CG, ABG, ACG, BC, ABCG & 1188 & 97.3\% & easy to execute, it requires less number of neurons for training. & Slow to use, computationally expensive, can’t be used to solve complex and analyze problems. \\ RBFNN [11] & Voltage and current & AG, BG, CG, ABG, ACG, BC, ABCG & 1188 & 99.3\% & Faster than BPNN, easy to use. & Not suitable for non-linear systems and large dataset \\  & Voltage and ACC, BCG, current & AG, BC, ABC, ABCG & & It can handle multi-dataset to classify and faults. & Expensive to implement, and learning can be slow, high high processing time if the network is extensive. \\  & Voltage and current & AG, BG, CG, ABG, ACG, BC, ABC, ABCG & & It can work with small datasets & Expensive to implement, and learning can be slow, high high processing time if the network is extensive. \\  & Voltage signal of 10 dB, 20 dB and 30 dB & Single, double and fault & 480 & 93.9, 96.8\% & It can work with small datasets & Not suitable for multiple datasets and low prediction accuracy. \\ CNN [16] & Three phase Voltage and current & 10 different fault condition & 92,077 & 99\% & Used to solve multi-channel component of software \\  & Voltage and current & & & & & Not suitable for multiple data and high processing time. \\ \hline \end{tabular}
\end{table} 
Table 7: Comparing different machine learning techniques for accuracy in fault classification.