# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 4 Results and Discussion  
In this study, we first created the RTDS IEEE 39-bus model. Then, we created 11 fault conditions for different fault impedance values [63]. We achieved this with the RSCAD simulation program running on the RTDS simulator [64; 65]. In the IEEE 39-bus system, fault detection can be performed by taking data from only eight different busbars. For this purpose, data were prepared by measuring the current and voltage values from each phase of eight different busbars (n = 3, 8, 11, 16, 20, 23, 29, 37). Outliers at different ratios were added to the fault data sets obtained from the IEEE 39-bus, and the response of the proposed method to outliers and noise was examined. Firstly, we added outliers at 0%, 5%, 10%, 15%, \(\ldots\), 50% and random samples were taken. Then, outliers were generated by preprocessing the target output. Next, based on the prepared data set, the accuracy of the CNN, LSTM, SVM, ELM, RVFLNs, Cauchy-M-RVFLN, and proposed methods was compared in the context of fault type and location detection. In this study, new data sets were created by adding outliers to the data previously prepared in the RTDS as described. According to this data set, the fault type classification accuracy results for the ORR-RVFLN method are as given in Figures 4 and 5. As can be seen in Figures 4 and 5, very close results are found for the classification accuracy of different types of faults for different fault impedances using noise-free and noisy data. In the noisy case, the accuracy of the method used decreases by approximately 4% to 10%. However, even when noisy data are added, the proposed method achieves a high rate of success. According to the results, it is observed that the proposed method gives correct results despite the noise. In light of the parameters given in this model, data were obtained for different fault impedances. The distances of the short circuits to the busbar were divided into seven different categories (0-50 m, 50-100 m, 100-150 m, 150-200 m, 250-300 m, 350-400 m, and  
Figure 3: IEEE 39-busbar model. >400 m), the smallest of which was between 0 and 50 m and the largest of which was more than 400 m. For each distance category, one hundred short circuits were simulated for different fault impedances and data were collected. Using the received data, the fault location was determined for four different fault impedances for each distance category with and without noise added. The fault location in the obtained data was determined by the ORR-RVFLN method. Table 2 shows the comparison of the ORR-RVFLN, Cauchy-M-RVFLN, RVFLN, CNN, LSTM, SVM, and ELM methods for single-phase ground fault location in the IEEE 39-bar system for distances between 0 and 50 m and greater than 400 m. This comparison was performed on both noisy and noiseless data for fault impedance values of 0 ohm, 30 ohm, 50 ohm, and 100 ohm. Table 2 shows that the fault location accuracy of the methods used was in the order of ORR-RVFLN, Cauchy-M-RVFLN, RVFLN, CNN, LSTM, SVM, and ELM. It is seen that the number of incorrect fault location predictions in the noise-added data is higher than for the noise-free data. The ORR-RVFLN method predicts the correct fault location more than the other methods. The ORR-RVFLN method makes 69 incorrect predictions for 100 faults between 0 and 50 m in the noise-added data with a fault impedance of 100 ohms, where the most incorrect fault locations are measured, while this value is 72 for the Cauchy-M-RVFLN method and 73 for the RVFLN method. The ORR-RVFLN method's performance for 700 faults with and without noise added, for each fault impedance and for a single-phase ground fault, is also evaluated. At a 0 ohm fault impedance, the average distance error for 700 faults with noise added is 5.68 m. At a 100 ohm fault impedance, the average distance error with noise added is 14.42 m. In addition, the misestimated distance error increases as the fault impedance increases. The average fault distance error is higher in the data without added noise.