Synchrophasor-based Fault Location Detection and Classification, in Power Systems, using Artificial Intelligence

With the introduction of sophisticated electronic gadgets which cannot sustain interruption in the provision of electricity, the need to supply uninterrupted and reliable power supply, to the consumers, has become a crucial factor in the present-day world. Therefore, it is customary to correctly identify fault locations in an electrical power network, in order to rectify faults and restore power supply in the minimum possible time. Many automated fault location detection algorithms have been proposed, however, prior art requires topological and physical information of the electrical power network. This thesis presents a new method of detecting fault locations, in transmission as well as distribution networks, using state-of-the-art machine learning algorithms on the real-time synchrophasor measurements obtained from the network. The proposed method first generates a bus admittance matrix from the synchrophasor data and then uses a neural network to identify the faulty buses. It is independent of network-specific data of the electrical power network. The proposed algorithm is evaluated using actual outage data from a real transmission system of Southwest Power Pool, in the year 2015. The results of the system implemented in python shows that the proposed method can detect fault locations with 100% accuracy