# Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances
## 4 Results and Discussion
### Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**True values** & Flicker & 1660 & 0 & 6 & 1 & 0 & 30 & 0 & 1 & 20 & 2 \\  & Harmonics & 0 & 1749 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\  & Impulsive transient & 1 & 0 & 1580 & 18 & 2 & 1 & 0 & 0 & 0 & 0 \\  & Interruption & 0 & 0 & 5 & 1670 & 1 & 2 & 0 & 38 & 0 & 0 \\  & Normal & 0 & 0 & 0 & 0 & 1745 & 0 & 0 & 0 & 0 & 0 \\  & Notch & 18 & 0 & 6 & 3 & 1 & 1570 & 4 & 0 & 6 & 0 \\  & Oscillatory transient & 14 & 0 & 2 & 0 & 0 & 25 & 1320 & 0 & 8 & 7 \\  & Sag & 0 & 0 & 2 & 9 & 0 & 0 & 0 & 1332 & 0 & 0 \\  & Spike & 9 & 0 & 0 & 0 & 0 & 0 & 10 & 0 & 1674 & 0 \\  & Swell & 0 & 0 & 1 & 0 & 0 & 0 & 12 & 0 & 0 & 1617 \\  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of the training data using GRU architecture, without noise. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**True values** & Flicker & 1749 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\  & Harmonics & 0 & 1750 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Impulsive transient & 0 & 0 & 1750 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Interruption & 0 & 0 & 0 & 1744 & 0 & 3 & 0 & 3 & 0 & 0 \\  & Normal & 0 & 0 & 0 & 1750 & 0 & 0 & 0 & 0 & 0 \\  & Notch & 0 & 0 & 0 & 5 & 0 & 1745 & 0 & 0 & 0 & 0 \\  & Oscillatory transient & 1 & 0 & 0 & 0 & 0 & 1745 & 3 & 1 & 0 \\  & Sag & 0 & 0 & 0 & 1 & 0 & 2 & 4 & 1743 & 0 & 0 \\  & Spike & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1749 & 0 \\  & Swell & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1750 \\  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix of the training data using the proposed CNN architecture, with noise. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**True values** & Flicker & 1640 & 0 & 8 & 2 & 0 & 38 & 0 & 2 & 26 & 4 \\  & Harmonics & 0 & 1748 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Impulsive transient & 2 & 0 & 1556 & 22 & 4 & 2 & 0 & 0 & 0 & 0 \\  & Interruption & 0 & 0 & 7 & 1658 & 2 & 3 & 0 & 46 & 0 & 0 \\  & Normal & 0 & 0 & 0 & 0 & 1740 & 0 & 0 & 0 & 0 & 0 \\  & Notch & 23 & 0 & 8 & 5 & 2 & 1551 & 6 & 0 & 9 & 0 \\  & Oscillatory transient & 18 & 0 & 4 & 0 & 1 & 33 & 1291 & 0 & 10 & 9 \\  & Sag & 0 & 0 & 3 & 12 & 1 & 1 & 0 & 1326 & 0 & 0 \\  & Spike & 12 & 0 & 0 & 0 & 0 & 0 & 13 & 0 & 1668 & 0 \\  & Swell & 0 & 0 & 2 & 0 & 0 & 0 & 15 & 0 & 0 & 1613 \\  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of the training data using BiLSTM architecture, without noise. Tables 12-16 provide detailed accuracy values for the proposed approach and the state-of-the-art methods. The accuracy values presented in these tables offer a comprehensive comparison of the proposed method against its alternatives. The proposed approach demonstrates superior performance across all evaluated classes, particularly evident in the accuracy metric, where it consistently outperforms the competing methods. This indicates a stronger ability to correctly identify and classify instances across various categories. Furthermore, the overall results illustrate that the proposed method effectively minimizes false positives and false negatives compared with both competing models, highlighting its robustness and reliability in classification tasks. Overall, these results underscore the efficacy of the proposed method in delivering enhanced accuracy and performance relative to existing state-of-the-art techniques.