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This dissertation highlights the growing interest in and adoption of machinelearning (ML) approaches for fault detection in modern power grids. Once afault has occurred, it must be identified quickly and preventative steps mustbe taken to remove or insulate it. As a result, detecting, locating, andclassifying faults early and accurately can improve safety and dependabilitywhile reducing downtime and hardware damage. ML-based solutions and tools tocarry out effective data processing and analysis to aid power system operationsand decision-making are becoming preeminent with better system conditionawareness and data availability. Power transformers, Phase Shift Transformersor Phase Angle Regulators, and transmission lines are critical components inpower systems, and ensuring their safety is a primary issue. Differentialrelays are commonly employed to protect transformers, whereas distance relaysare utilized to protect transmission lines. Magnetizing inrush, overexcitation,and current transformer saturation make transformer protection a challenge.Furthermore, non-standard phase shift, series core saturation, lowturn-to-turn, and turn-to-ground fault currents are non-traditional problemsassociated with Phase Angle Regulators. Faults during symmetrical power swingsand unstable power swings may cause mal-operation of distance relays andunintentional and uncontrolled islanding. The distance relays also mal-operatefor transmission lines connected to type-3 wind farms. The conventionalprotection techniques would no longer be adequate to address the abovechallenges due to limitations in handling and analyzing massive amounts ofdata, limited generalizability, incapability to model non-linear systems, etc.These limitations of differential and distance protection methods bring forwardthe motivation of using ML in addressing various protection challenges.

Data-driven Protection of Transformers, Phase Angle Regulators, and Transmission Lines in Interconnected Power Systems