# Wide-Area Protection System for Radial Smart Distribution Networks
## 4 Simulation Study  
The simulation study is conducted in DIGSILENT PowerFactory 15.7.1 and MATLAB 2019b on a six-bus distribution network shown in Figure 3. Detailed data of the network loads and line sections are reported in [34]. A 4.5 MW DFIG with its detailed transient parameters are illustrated in Table 1 in parallel with a 15 MVA Battery Energy Storage System (BESS) added to Bus 6 of the network. The BESS is the slack bus of the network in the islanded mode of operation. In order to train the UFDMs, two sets of training data are generated; the first set corresponds to the fault conditions, and the second set corresponds to the non-fault conditions. Details regarding these sets are illustrated in Tables 2 and 3. The simulations were performed using an Electromagnetic Transient (EMT) analysis in DlgSILENT, running for 2.5 s for each condition. During these simulations, data from the relays (including three-phase Root-Mean-Square (RMS) values of the voltages and currents, as well as phase angles) are extracted and saved. Subsequently, data from three cycles before the disturbance and one cycle after the disturbance are used to calculate the positive sequence values, as shown in Equation (8). The relevant features (superimposed and mean values of the signals) are then determined, resulting in six features for each condition. The  
Figure 3: Single-line diagram of the 6-bus test network. Figure 2: A simple five-bus radial test network. features are the mean and superimposed values of the voltage, current, and phase angle using (5) and (6). The input data for the ANN are not explicitly normalized; however, as they are extracted from per-unit values of the network signals, they are inherently normalized to some extent. Testing the method in the presence of noise is planned for future work. Nevertheless, since the features are derived from the mean or integral of the network signals, the method is anticipated to exhibit robustness in the presence of noise. Figure 4 shows the 2D scatter plot of the superimposed values (Voltage-Current, Voltage-Phase, and Current-Phase) for all the samples. It is evident from the graph that distinct classes of the sampled data are discernible. Figure 5 presents a zoomed-in view of the 2D scatter plot of the superimposed voltages and current, providing a better illustration of the class boundaries. It is expected that utilizing all the six features significantly enhances the ability of the ANN to differentiate between classes and understand the bounding contours. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Rated Voltage (kV) & 6.6 \\ Capacity (MW) & 4.5 \\ Inertia (kg\(\cdot\)m\({}^{2}\)) & 245 \\ Locked rotor current (pu) & 3.850 \\ Locked rotor torque (pu) & 0.185 \\ Torque at stalling point (pu) & 2.050 \\ Stator reactance (pu) & 0.010 \\ Stator resistance (pu) & 0.021 \\ Magnetizing reactance (pu) & 4.441 \\ Rotor reactance (pu) & 0.100 \\ Rotor resistance (pu) & 0.100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed data of the transient parameters of the DFIG. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Details** & **Number** \\ \hline Operation mode & Islanded and connected & 2 \\ Fault resistance (\(\Omega\)) & 0.1, 1, 2, 5, 10 & 5 \\ Fault section (line) & L12, L23, L34, L45, L56 & 5 \\ Fault location on the section & \(10,50,90\) & 3 \\ (\%) & R12, R21, R23, R32, R34, R43, & 10 \\ Relays & R45, R54, R56, R65 & 1500 \\ Total fault samples & 1500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of generated fault samples.