2023-11-202023-11-202023-08https://hdl.handle.net/2346/96817This research answers the question, “With the current knowledge of power systems engineering, would protection problems be solved the same way?” Today’s protection methodologies follow a very narrow approach that relies on overcurrent detection, and these are traditionally used in power systems with radial power flow. In other words, when a protection scheme senses a higher than intended current, it limits or disables current flow; however, when this type of problem is approached with today’s innovative problem solving solutions, a lens is provided to see issues differently. In Advanced Grid Operations research, a Real-Time Digital Simulator is used to simulate, in real-time, several different types of faults on different types of power systems to create a repository of fault data used to train a Machine Learning algorithm. Machine Learning Protective Relaying aims to ultimately provide additional information that can be leveraged to solve protection problems regarding traditional protective relays, renewable energy, and other roadblocks.Application/pdfenProtection MethodologyFault DetectionPower SystemRenewable EnergyInnovationReal-Time SimulationMachine LearningThesisAccess is not restricted.