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In this paper, an efficient method based on the Kalman filter (KF) theory is proposed for fast and accurate detection and compensation of current transformer (CT) saturation. During the unsaturated regions, the algorithm uses the current sample points together with extended Kalman filter (EKF) for obtaining a model, which suitably represents the current signal behavior. The acquired model is used to reconstruct the original current waveform during the saturated regions. The onset and end points of the CT saturation intervals are detected by defining a criterion based on the estimation error of the EKF algorithm. The proposed method is very profitable as it requires only one sample point to detect the CT saturation and it also copes with the problems of measurement noises and harmonics. These advantages are confirmed by a large number of test cases obtained using PSCAD/EMTDC software as well as by real-time hardware implementation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]