Phasor Measurement Unit Based Fault Detection using Neural Network

This study focuses on the creation of a fault detection system using phasor measurement units (PMUs) and neural network. PMUs are devices that can measure electrical quantities in power system, such as positive sequence voltage, current phasors, and frequency with timestamp. PMU data can be used by fault detection system to determine the occurrence of fault and location of faults in the power system. Neural network is used in this study to analyse PMU data and detect power system faults. The neural network is trained using dataset of PMU for simulating power system faults. Neural network is giving high accuracy for detection of place of fault and type of fault identification. The proposed system has been evaluated and compared with existing fault detection technique using simulation studies. The result shows that the proposed system can detect faults in the power system and outperforms existing techniques in terms of accuracy and detection speed. This research could lead to the development of more efficient and reliable fault detection systems for power system, resulting in better power system operation and reliability.