Fault detection and classification for compensating network using combination relay and ANN

The goal of this research is to focus and adopt a fast, accurate and reliable fault detection technique and classification method for the high voltage transmission line. The proposed method reduces the outage time and hence this eliminates any possible damage to the other parts of the system. First, detection of the fault is carried out using a new proposed technique that combines three type of relays. Second, the technique isolates the faulty part in a very fast time frame. Finally, classifying the fault is carried out by data recorded using Digital Signal Processing (DSP) and Artificial Neural Network (ANN) based on different ways. The input training data of the recording devices was sampled using Digital Signal Processing (DSP). In this research the data collected from the recorders will be used to classify the fault only because the time is not an important factor as in fault detecting and clearing. Also, all types of faults are investigated for the fault classification. Three methods are used (Phase Current sampling, Phase Shift of the Phase Voltage sampling and Phase Voltage sampling) to evaluate the efficiency, accuracy and the analysis the mean square error.