Current differential based wide area protection (WAP) has recently been proposed as a technique to increase the reliability of protection systems. It increases system stability and can prevent large contingencies such as cascading outages and blackouts. This paper describes how power differential protection (PDP) can be used within a WAP and shows that the algorithm operates correctly for all types of system faults whilst preventing unwanted tripping, even if the data has been distorted by CT saturation or by data mismatches caused by delays in theWAP data collection system. The PDP algorithm has been simulated and tested on an Iranian 400kV transmission line during different fault and system operating conditions. The proposed operating logic and the PDP algorithm were also evaluated using simulation studies based on the Northern Ireland Electricity (NIE) 275 kV network. The results presented illustrate the validity of the proposed protection.

The paper represents the development of an adaptive multi-criteria algorithm based on fuzzy approach for the decision making part of the differential transformer protection schemes. At first some effective phenomenon have been examined. Then off-line examination results have been implemented for adapting the relay parameters such as fuzzy functions, weighting factors and tripping threshold to relay conditions and transformer characteristics. Self-adjustment of relay parameters has improved the speed of operation, dependability and security of relay. Different tests have indicated satisfactory results and less than half a cycle for the mean of operating time.

An Indirect Symmetrical Phase Shift Transformer (ISPST) represents both electrically connected and magnetically coupled circuits, which makes it unique compared to a power transformer. Effective differentiation between transformer inrush current and internal fault current is necessary to avoid incorrect differential relay tripping. This research proposes a system that uses a Chebyshev Neural Network (ChNN) as a core classifier to distinguish such internal faults. For simulations, we used PSCAD/EMTDC software. Internal faults and inrush have been simulated in various ways using various ISPST parameters. A large, simulated dataset is used, and performance is recorded against different sized ISPSTs. We observed an overall accuracy greater than 99%. The ChNN classifier generated exceptionally favorable results even in case of noisy signal, CT saturation, and different ISPST parameters.

The uncertainty in connection status of distributed generation and network structure complicates the adjustment of the overcurrent relays in distributing network. With the increase in number of modes of operation, the investigation area faces limitations and becomes even pointless, making it almost impossible to reach a unified host of setting applicable for all possible modes. Yet, the conditional situations could be overlooked and the investigation could be objective-oriented once the differential protection is maintained and the faults are taken care of instantly. The differential protections must be installed with the minimum number and in the proper places so that the incompatible conditions would be eliminated. As such, a new approach based on additional methods with regard to the nature of differential protection and its impact on identifying incompatible conditions is recommended. The outcome of such approach is a set of candid lines for installing the differential protections and thus the convergence of protective coordination of overcurrent relays. This approach has been simulated for the ringed distributed active network and, as the outcome reveals, the recommended approach with the proper placement of differential protections along with the unified setting for all relays makes the constant protection coordination possible.

In this paper, a new algorithm based on Kalman Filter (KF) is proposed for improving the reliability of power transformer differential protection. The three-phase currents of the transformer at the energization side are first estimated by KF. Three residual signals which are the differences between the measured (by current transformers) and estimated (by KF) are defined and considered as the decision criteria to discriminate the fault and inrush conditions. During the transformer energization, the residual signals are almost zero. However, in fault conditions, the residual signals exceed a determined threshold value and a trip command will be subsequently issued. The performance of the proposed method is verified using simulations in MATLAB and PSCAD software.

Power transformers are the most important components of a power system, so their protection is a critical issue. This paper proposes a novel and efficient algorithm based on the high-frequency components of the differential current signal to discriminate between the magnetizing inrush currents and the internal faults. After detecting the over-current in the differential current signals, samples of a quarter of a cycle of the signal are recorded. Then, discrete wavelet transform (DWT) is applied to the recorded signals, and the details of the wavelet transform output are extracted. Because of the existence of the high-frequency transients in the internal fault current signals, the wavelet transform outputs of the internal fault signals have more fluctuations than that of the inrush current signals. By calculating the standard deviation of the wavelet transform output, the fluctuations can be quantified. Therefore, the standard deviation of the wavelet transform output can be used as a criterion to discriminate between the internal faults and the magnetizing inrush currents. The proposed algorithm has a very low computational burden, and it uses only a quarter of a cycle of the differential current signals. This guarantees the high speed of the proposed algorithm. The proposed algorithm is tested by different conditions of the internal faults and the inrush situations, and it successfully identifies the true situation with high accuracy in all conditions. The simulation results show the superior specifications of the proposed algorithm.

Power transformers are the most important components of a power system, so their protection is a critical issue. This paper proposes a novel and efficient algorithm based on the high-frequency components of the differential current signal to discriminate between the magnetizing inrush currents and the internal faults. After detecting the over-current in the differential current signals, samples of a quarter of a cycle of the signal are recorded. Then, discrete wavelet transform (DWT) is applied to the recorded signals, and the details of the wavelet transform output are extracted. Because of the existence of the high-frequency transients in the internal fault current signals, the wavelet transform outputs of the internal fault signals have more fluctuations than that of the inrush current signals. By calculating the standard deviation of the wavelet transform output, the fluctuations can be quantified. Therefore, the standard deviation of the wavelet transform output can be used as a criterion to discriminate between the internal faults and the magnetizing inrush currents. The proposed algorithm has a very low computational burden, and it uses only a quarter of a cycle of the differential current signals. This guarantees the high speed of the proposed algorithm. The proposed algorithm is tested by different conditions of the internal faults and the inrush situations, and it successfully identifies the true situation with high accuracy in all conditions. The simulation results show the superior specifications of the proposed algorithm.