Modeling and Simulation of Time Domain Reflectometry Signals on a Real Network for Use in Fault Classification and Location

Abstract

Today, the classification and location of faults in electrical networks remains a topic of great interest. Faults are a major issue, mainly due to the time spent to detect, locate, and repair the cause of the fault. To reduce time and associated costs, automatic fault classification and location is gaining great interest. State-of-the-art techniques to classify and locate faults are mainly based on line-impedance measurements or the detection of the traveling wave produced by the event caused by the fault itself. In contrast, this paper describes the methodology for creating a database and a model for a complex distribution network. Both objectives are covered under the paradigm of the time-domain pulse reflectometry (TDR) principle. By using this technique, large distances can be monitored on a line with a single device. Thus, in this way a database is shared and created from the results of simulations of a real and complex distribution network modeled in the PSCAD TM software, which have been validated with measurements from an experimental test setup. Experimental validations have shown that the combination of the TDR technique with the modeling of a real network (including the real injector and the network coupling filter from the prototype) provides high-quality signals that are very similar and reliable to the real ones. In this sense, it is intended firstly that this model and its corresponding data will serve as a basis for further processing by any of the existing state-of-the-art techniques. And secondly, to become a valid alternative to the already well-known Test Feeders but adapted to work groups not used to the electrical world but to the environment of pure data processing.

TransSiamese: A Transformer-Based Siamese Network for Anomaly Detection in Time Series as Approach for Fault Location in Distribution Grids

“…In this study, we have used an existing database [14] containing 200 signals, which represent the grid's response to injected pulses using Time Domain Reflectometry (TDR) [7].…”

“…The authors claim using the pre-fault and fault signals to train the Transformer. The pre-fault is the signal of the electric network's response to the injection of the pulse (TDR) when the grid is operating normally, and fault signal is the response when the grid is in fault state [7]. The propagation and reflection of TDR signals through an electrical network are very similar as long as there are no significant impedance changes in the network (faults).…”