REGULARIZATION TECHNIQUES TO IMPROVE EFFICIENCY OF TIME DOMAIN PROTECTION

One component of time domain protection in a power system is estimating the location of a fault. In an embodiment, a multi-objective problem is formulated that comprises a non-smoothness penalization function that drives the primary objective function for fault location estimation towards a solution that respects smoothness between the inputs and outputs of a machine-learning model. This technique improves the accuracy, blind zone, and speed of state-of-the-art techniques, in the context of time domain protection, as well as for other regression tasks. In an additional or alternative embodiment that is specific to time domain protection, the multi-objective problem may comprise a phasor-deviation penalization function that drives the primary objective function towards a solution that minimizes deviations in phasor values. The trained machine-learning model may be executed in a line protection system to determine whether or not to trip a circuit breaker of a power line.