Fast Discrimination of Transformer Magnetizing Current From Internal Faults: An Extended Kalman Filter-Based Approach

Abstract

Differential protection is the most common type of protection in power transformers. However, inrush current due to transformer energization may appear as fault current to the protective relay, causing a false trip of the transformer. Therefore, discrimination of the transformer inrush current from internal faults is necessary to improve the security of the protection scheme. In this paper, a new method for discrimination of the transformer inrush current from an internal fault current is proposed. First, the nonlinear state-space model of a real single-phase transformer is derived, which incorporates the nonlinear phenomena of hysteresis and magnetic saturation. Based on the derived model, an Extended Kalman Filter (EKF) is used for estimation of the primary winding current. A residual signal is defined as the difference between the measured and estimated currents. When a healthy transformer is energized, the EKF perfectly estimates the primary winding current and, hence, the residual signal is almost zero. However, when the transformer is faulty, the EKF cannot effectively estimate the current due to the existing large model mismatches. Consequently, a large residual signal is created. The superiorities of the proposed algorithm are demonstrated using different experimental scenarios.