Fast Accurate Phasor Estimation in Less than One Cycle using Neural Networks

Estimation of phasors holds significant importance within power system protection applications. Due to the inher-ent inductive characteristics of electric power systems, fault currents often exhibit transient oscillations, such as decaying DC offset (DDC), higher-order harmonics, and noise. Fourier analysis stands out as a widely adopted technique for phasor estimation calculations. Nevertheless, when dealing with decaying DC currents, which may persist for up to 5 cycles (in the case of a 50/60Hz system), the accuracy of Fourier analysis diminishes significantly. An inaccurate phasor estimation could lead to malfunction or misoperation of protective devices, most notably distance protection relays, an undesirable outcome. Therefore, it becomes imperative to address the issue of DC offset signal mitigation. While various analytical methods have been proposed in recent years, there exists a pressing need to explore less complicated data-driven approaches. In this paper, we aim to employ two neural network methods to estimate the current in less than one cycle, taking into account second-order DDC and system frequency deviations. Compared with other presented data-driven methods, the proposed scheme is only uses a half cycle of the fault current signal with less number of trained parameters and without applying encoder/decoder methods.