Wide-area disturbances are power outages occurring over large geographical regions and causing interruptions of the electric supply to residential, commercial, and industrial users. Historically, wide-area disturbances have had adverse impact on societies. Researches show that power system's protection schemes play a critical role in wide-area disturbances. Incorrect operations of power system's protection schemes have contributed to the spread of disturbances. Mal-operations of protection schemes have contributed in the degradation of 70-80 percent of the wide-area disturbances. This thesis shows that the mal-operation of protection system can be due to voltage and signal distortion (CT & CVT misbehavior), poor fault diagnostics, or wrong decision making. This thesis then introduces application of artificial intelligence based protective methods to reduce and mitigate wide area disturbances: ANN based schemes are used as function approximation for signal correction; ANFIS based methods are used as classifier for fault diagnostics for a wide range of applications (distance protection, load blinder, power swing blocking, auto-reclosing); and fuzzy logic is used to aid decision making. Simulation results for the designed algorithms show considerable capability of artificial intelligence based methods to improve performance of protection system and prevent them from mal-operation, which could consequently reduce and mitigate wide area disturbances.