## 4 Simulation Result and Discussion
### Analysis of Case Studies  
#### 4.3.1 Case I: Base Case  
The proposed TempCNN-LSTM is used in this case study to categorize all the phenomena that are present in the STVS assessment. The performance of the proposed model is then compared with other deep learning and conventional machine learning models for both the test systems. According to the data generation by TDS, the training data set for the IEEE 9 Bus test system, there are 19,450 labeled samples: 3514 unstable as class 0, 13,401 stable as class 1, and 2535 stable-with FIDVR as class 2. Similarly, for the training data set for the New England 39 Bus test system, there are 20,500 labeled samples: 4062 unstable as class 0, 12,207 stable as class 1, and 4231 stable-with FIDVR as class 2. All the analysis in this case is conducted with the assessment time of 0.25 s. For the IEEE 9 Bus system, the TempCNN-LSTM model consists of two convolution layers each with 64 units. The TempCNN-LSTM model for New England 39 Bus, on the other hand, is made up of two convolution layers, each with 128 units. For both systems, the filter length of each convolutional layer is set to 3. The learned features are flattened into one long vector after the convolution step. It then passes through an LSTM layer in a time-distributed manner. The LSTM model consists of a single layer with 64 and 128 hidden units for the IEEE 9 Bus system and New England 39 Bus system, respectively, one dense layer and an output layer classify the STVS state of the system. In each layer of both systems' models, the ReLU activation function is used for non-linear mapping. The SoftMax activation in the output layer gives the probability distribution of each class label, and the class with the highest probability is selected for the particular input samples. The overall model configurations for both test systems are shown in Figure 11. The parameter setting for training the proposed TempCNN-LSTM model is shown in Table 4. The units in the convolutional layers, filter size, hidden units in the LSTM layer, and dense layer are determined using hyper-parameter optimization using the grid search technique using five-fold cross-validation. Table 5 shows the performance of the proposed TempCNN-LSTM model for the prediction of the STVS state of the system in terms of average accuracy, precision, recall, F1-score, training time and testing time. The results show that the proposed model can predict the STVS state with the average accuracy of 98.66% and 96.95% for the IEEE 9 Bus system and New England 39 Bus system, respectively, for a multi-class classification problem. The diagonal of the confusion matrix of the test results, as shown in Figure 12, includes the correctly classified samples of each label for both test systems. In this study, the performance of the proposed model is compared with other popular deep learning and machine learning models such as TempCNN, LSTM, SVM and DT. The result in Table 5 shows that the proposed model outperformed significantly for the multi-class classification task required for the assessment of STVS. Apart from classification accuracy, the proposed model outperformed other models with precision of 98.74%, recall of 98.66%, and F1-score of 98.68% for the IEEE 9 Bus test system with an uneven class distribution of test samples of \begin{table}
\begin{tabular}{l l} \hline \hline Load level & \(\pm\)20\% of initial loading \\ Fault lines & All the transmission lines \\ Load model & Complex load model in DIgSILENT \\ Load dynamics & 0–80\% of induction motor load \\ Fault locations & 0–80\% of all fault lines \\ Fault type & Three-phase bolted fault \\ Fault duration & 0.1 s \\ Simulation time & 5 s \\ Length of OTW & 0.25 s of post fault voltage trajectories \\ Sampling per time step & 0.01 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Configuration of data set generation on test systems. each category.