# Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances
## 4 Results and Discussion
### Quantitative Evaluation  
Tables 2-6 present the confusion matrices for the proposed CNN, along with those of the compared state-of-the-art methods on datasets without noise. Additionally, Tables 7-1  
detailed analysis highlights the classification results and also showcases the model's ability to accurately predict various classes of PQDs. By systematically comparing the confusion matrices of the proposed method with those of leading approaches, the proposed method can effectively evaluate key performance metrics such as precision, recall, and overall accuracy for each class. The results reveal that the introduced method consistently outperforms its competitors, achieving higher precision and recall rates across multiple PQD classes. This indicates that the proposed approach enhances classification accuracy and also improves the model's reliability in distinguishing between different categories of PQDs. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**True values** & Flicker & 1740 & 0 & 1 & 0 & 2 & 0 & 2 & 3 & 0 & 1 \\  & Harmonics & 0 & 1750 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Impulsive transient & 0 & 0 & 1728 & 0 & 10 & 0 & 12 & 0 & 0 \\  & Interruption & 0 & 0 & 10 & 1725 & 8 & 0 & 0 & 7 & 0 & 0 \\  & Normal & 0 & 0 & 0 & 0 & 1750 & 0 & 0 & 0 & 0 \\  & Notch & 0 & 0 & 3 & 0 & 30 & 1715 & 0 & 0 & 0 & 2 \\ Oscillatory transient & 0 & 0 & 8 & 0 & 5 & 0 & 1735 & 0 & 0 & 2 \\  & Sag & 0 & 0 & 15 & 5 & 6 & 0 & 0 & 1724 & 0 & 0 \\  & Spike & 0 & 0 & 3 & 0 & 20 & 0 & 0 & 0 & 1728 & 0 \\  & Swell & 0 & 0 & 1 & 0 & 5 & 0 & 3 & 0 & 0 & 1740 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of the training data using the ResNet50 architecture, without noise. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**True values** & Flicker & 1747 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 1 \\  & Harmonics & 0 & 1750 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Impulsive transient & 0 & 0 & 1740 & 3 & 2 & 1 & 1 & 3 & 0 & 0 \\  & Interruption & 0 & 0 & 3 & 1744 & 0 & 0 & 3 & 1 & 0 & 0 \\  & Normal & 0 & 0 & 0 & 0 & 1748 & 0 & 0 & 0 & 0 & 0 \\  & Notch & 0 & 0 & 5 & 0 & 0 & 1745 & 0 & 0 & 0 & 0 \\  & Oscillatory transient & 0 & 0 & 2 & 2 & 0 & 4 & 1744 & 1 & 0 & 1 \\  & Sag & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1744 & 5 & 1 \\  & Spike & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1745 & 0 \\  & Swell & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 1 & 0 & 1747 \\  & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix of the training data using the proposed CNN architecture, without noise. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**True values** & Flicker & 1747 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 1 \\  & Harmonics & 0 & 1750 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Impulsive transient & 0 & 0 & 1740 & 3 & 2 & 1 & 1 & 3 & 0 & 0 \\  & Interruption & 0 & 0 & 3 & 1744 & 0 & 0 & 3 & 1 & 0 & 0 \\  & Normal & 0 & 0 & 0 & 0 & 1748 & 0 & 0 & 0 & 0 & 0 \\  & Notch & 0 & 0 & 5 & 0 & 0 & 1745 & 0 & 0 & 0 & 0 \\  & Oscillatory transient & 0 & 0 & 2 & 2 & 0 & 4 & 1744 & 1 & 0 & 1 \\  & Sag & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1744 & 5 & 1 \\  & Spike & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1745 & 0 \\  & Swell & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 1 & 0 & 1747 \\  & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of the training data using the CNN architecture in [7], without noise.