Today, the classification and location of faults in electrical networks continues to be a topic of great interest. Faults are a major problem mainly due to the working time spent to detect, locate and repair the cause of the fault. So, automatic fault classification and location is gaining great interest due to the cost and time savings compared to conventional location techniques. There are several state-of-the-art techniques that help to classify and locate faults. These techniques are mainly based on line-impedance measurements or in the detection of the traveling wave produced by the event caused by the fault itself. thirdly, this paper shows a technique that has given very good results based on time-domain pulse reflectometry (TDR). The physical principle is based on high frequency pulses injected into the network. These pulses are propagated through the network and returned to the injector bringing information about the state of the network. Using this technique, large distances can be monitored on a line with a single device. In addition, a real complex network and a real injector have been modeled in PSCADTM software. The combination of the TDR technique with the modeling of a real network results in high quality signals. These signals, which are very similar to the real ones, can be used for further processing by any of the existing state-of-the-art techniques, such as Neural Networks. Unlike the IEEE-13 network model, a real complex network has been modeled in this work. In addition, a real injector and a real network coupling filter have been modeled. This technique allows the verification of the model by comparing the modeled signals and the real ones obtained in the field. In addition, this work provides a database of simulated signals generated by simulation that can be used for experiments.