Sampling rate of digital fault recorders influence on fault diagnosis

A case study of fault classification in transmission lines using artificial neural networks (ANN) is presented. The database is built from current and voltage waveform samples obtained from fault simulations with the ATP. Utility companies usually have digital fault recorders with different sampling rates, so it is important to evaluate how good the classifier is when the sampling rate changes, this is the main purpose of the paper. A routine to reduce the sampling rate with no loss of accuracy in classifying faults was implemented.