# Wide-Area Protection System for Radial Smart Distribution Networks
## 3 Proposed Protection Plan
### Fault Detection  
Due to the dynamics of the DER-integrated networks, conventional overcurrent protection schemes are facing challenges for proper fault detection procedure. The main reason for these network dynamics is the operation mode of the network, whether it is connected to or isolated from the main grid. During connected mode of operation, the fault current magnitudes contributed from the main grid is significant, and the conventional overcurrent principle is still trustworthy, but during islanded mode of operation, in the lack of main grid, the fault current magnitudes is less significant. Despite the lower fault current magnitudes in the islanded mode of operation, the voltage drop during faults is more noticeable. This assumption is the obvious result of the equivalent impedance of the network as given by Equation (4):  
\[|\Delta V|=\left|Z_{e}\times I_{f}\right| \tag{5}\]  
where \(\Delta V\), \(Z_{e}\), and \(I_{f}\) are the voltage drop, equivalent network impedance, and fault current. In connected mode of operation, due to the presence of various parallel routes in the network, the equivalent impedance is much smaller compared to the islanded mode of operation where the fault current is only contributed by DERs. In other words, even though high fault current magnitudes are absent during the islanded mode of operation, the voltage drop is significant and aids in fault detection. Hence, in this paper, both the voltage and current signals are utilized for the fault detection procedure. To overcome the variation in fault current behavior, a Universal Fault Detection Module (UFDM) is designed by employing Artificial Neural Networks (ANNs) that are trained to detect fault occurrence. The utilization of a universal module helps to reduce the complexity of the training procedure of the ANN; meanwhile, it helps well in the fault management procedure discussed in Section 3.2. The UFDMs are installed at the same locations as the conventional relays within the network. In other words, UFDMs replace the traditional fault detection modules, which primarily utilize the coordinated overcurrent protection principle. For each UFDM, the input features are a combination of voltage and current features. Each UFDM has six input features as follows:  
- Mean of the voltage magnitude (\(V^{m}\));  
- Integration of the superimposed voltage (\(V^{si}\));  
- Mean of the current magnitude (\(I^{m}\));  
- Integration of the superimposed current (\(I^{si}\));  
- Mean of phase angle between the voltage and current (\(\varphi^{m}\));  
- Integration of the superimposed phase angle between the voltage and current (\(\varphi^{si}\)). Calculations of the above-mentioned features are performed using the below formulations:  
\[S^{m}=\frac{1}{T}\int S^{po}(t).dt \tag{6}\]  
\[S^{si}=\int(S^{po}(t)-S^{pr}(t)).dt \tag{7}\]where \(S\) is the signal that can be the current voltage or phase angle, and the superscripts \(po\) and \(pr\) refer to the post-disturbance and pre-disturbance conditions, respectively. The mean value is a useful feature that can represent the condition of the network during disturbances during the occurrence of large disturbances like motor starting, though this feature might misrecognize non-fault disturbances with faults. Hence, the integrative of the superimposed values are also considered. The integrative function helps to neglect the transient, behavior and the superimposed operation helps the method to be independent from the load variations. This paper is mainly focused solely on three phase faults using the positive sequence of system voltage and currents. However, this assumption also significantly contributes to the ability to detect unbalanced faults. By using the positive sequence components, which are sensitive to all types of faults, it is ensured that the method can be extended to respond to different fault conditions, making it more versatile and robust. The ANN-based UFDM is trained based on these features to detect fault or non-fault conditions.