## 5 Conclusions  
This work proposes a method based on deep learning-based time series classification algorithm TempCNN-LSTM for the online assessment of STVS of the power system with RESs. The proposed method can classify the STVS state: stable or unstable or stable-with FIDVR phenomenon using the post-fault voltage trajectories within a short OTW. The findings of the study are summarized as follows:  
* The performance of the proposed model evaluated in terms of classification accuracy shows 98.66% accuracy for the IEEE 9 Bus test system and more than 96.95% accuracy for the New England 39 Bus Test System with efficient computational time for the online assessment of STVS. * The proposed model shows the superior performance compared to other deep learning models (TempCNN and LSTM) and conventional machine learning models (SVM and DT). The performance of the proposed model in terms of classification accuracy, precision, recall and F1-score is significantly high compared to other classification models. * The proposed model is more tolerant to the PMUs measurement error/losses and thus is suitable for online assessment of STVS. * The accuracy of the proposed model under the variation of different assessment time shows the superior performance compared to the existing state-of-art deep learning-based time series classification models (TempCNN and LSTM) and machine learning models (SVM and DT). Furthermore, for all these models, the accuracy increases with the increase in the OTW length. * Using the deep learning-based transfer learning approach, the proposed model can predict the STVS state of the system under topology change condition (N-1 contingency) with more than 97% accuracy for both the test systems with few labeled samples. It \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**IEEE 9 Bus**} & \multicolumn{2}{c}{**New England 39 Bus**} \\ \cline{3-6}  & & **With TL** & **Without TL** & **With TL** & **Without TL** \\ \hline \multirow{7}{*}{TempCNN} & Accuracy & 96.15\% & 76.92\% & 95.59\% & 63.73\% \\  & Precision & 98.07\% & 87.23\% & 95.13\% & 72.66\% \\  & Recall & 96.15\% & 76.92\% & 95.59\% & 63.72\% \\  & F1-Score & 96.75\% & 74.69\% & 95.28\% & 56.16\% \\  & Re-Training Time & 8.39 s & - & 9.53 s & - \\  & Testing Time & 48.6 ms & 71.5 ms & 48 ms & 142 ms \\ \hline \multirow{7}{*}{LSTM} & Accuracy & 96.15\% & 61.54\% & 94.12\% & 80.88\% \\  & Precision & 96.15\% & 78.71\% & 94.34\% & 82.92\% \\  & Recall & 96.15\% & 61.54\% & 94.12\% & 80.88\% \\  & F1-Score & 96.15\% & 55.41\% & 93.65\% & 79.88\% \\  & Re-Training Time & 18.1 s & - & 26.5 s & - \\  & Testing Time & 52 ms & 188 ms & 54 ms & 451 ms \\ \hline \multirow{7}{*}{SVM} & Accuracy & 94.23\% & 59.61\% & 92.64\% & 82.35\% \\  & Precision & 88.84\% & 79.01\% & 87.19\% & 86.20\% \\  & Recall & 94.23\% & 59.61\% & 92.06\% & 82.35\% \\  & F1-Score & 91.44\% & 54.61\% & 89.83\% & 81.58\% \\  & Re-Training Time & 740 ms & - & 3.54 s & - \\  & Testing Time & 2 ms & 14 ms & 7 ms & 71 ms \\ \hline \multirow{7}{*}{DT} & Accuracy & 92.31\% & 46.15\% & 92.65\% & 75.00\% \\  & Precision & 97.98\% & 84.62\% & 92.70\% & 83.65\% \\ \cline{1-1}  & Recall & 92.31\% & 46.15\% & 92.65\% & 75.00\% \\ \cline{1-1}  & F1-Score & 95.06\% & 49.36\% & 92.57\% & 74.74\% \\ \cline{1-1}  & Re-Training Time & 1.41 s & - & 3.29 s & - \\ \cline{1-1}  & Testing Time & 1 ms & 1 ms & 1 ms & 2 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._eliminates the need to train different learning models. Furthermore, it reduces the computational time required for re-training the model for online application. Our future research will investigate the control measure to mitigate the STVS problem in the power system using a data-based approach suitable for online application. Conceptualization, A.A., S.N. and A.S.; methodology, A.A., S.N. and A.S.; software, A.A.; validation, A.A., S.N. and A.S.; writing--original draft preparation, A.A., S.N. and A.S.; writing--review and editing, A.A., S.N.