This paper describe a new ultra-high speed protection algorithm for traveling-wave distance protection for transmission lines, based on pattern recognition of the first wavefront that arrives at the relay location due to fault. The algorithm uses principal component analysis (PCA) to preprocess data from the power system in order to eliminate redundant information and enhance hidden patterns in the traveling waves for internal and external faults. The algorithm was proven using PSCAD/EMTDC simulations in a three-phase 400-kV power system considering critical fault cases. The results show the feasibility to implement this algorithm for transmission-line ultra-high speed protection

This paper describe a new algorithm for transformer differential protection, based on pattern recognition of the differential current obtained as the phasor sum of the current-transformer secondary currents. The algorithm uses principal component analysis to preprocess data from the power system in order to eliminate redundant information and enhance hidden pattern in differential current to discriminate between internal faults (transformer differential protection zone) from inrush and overexcitation conditions. The algorithm was proven using PSCAD/EMTDC simulations in a three-phase power system considering critical fault cases. The results show the feasibility to implement this algorithm for transformer differential protection.

This paper describes a differential algorithm for the protection of a three-phase transformer to differentiate transient phenomena, such as inrush currents and external faults from solid and turn-to-turn faults. This algorithm is based on the behavior of the second central moment (SCM) to identify the kind of event. Instantaneous differential currents are used as input signals to the algorithm once they are filtered and normalized. The algorithm compares the magnitude of the SCM with an established threshold based on the binomial distribution to achieve a successful identification of the event. If the magnitude of the SCM is above the limit, the algorithm identifies the event as a fault condition. Otherwise, the event is determined as a transient event or a steady-state condition. The threshold is setting-free if the transformer parameters are changed or if the harmonic content of the differential current varies. Also, the threshold does not have to be recalculated if the power system is modified. The D index was introduced to improve the fault-detection time when the current transformers experience saturation. This index uses the magnitude of the SCM as a basis and is compared with an established limit to detect the fault current. Both indices run in parallel, and if any of them does cross its respective threshold, a fault condition is determined. The algorithm is tested in a real-time digital simulator and is implemented in MATLAB. The algorithm was compared against the conventional differential protection with harmonic restraint to evaluate the performance of both indices in critical conditions. The results validate the performance of the algorithm and indicate that it can be the basis of a new algorithm to protect power transformers.

Information about typical fault resistances of transmission lines will help improve the settings of distance relays. This paper proposes an offline method for estimating the fault resistance of transmission lines using the COMTRADE fault files available from digital distance relays. The proposed method uses the fault records at both ends of a line, but synchronization of the data sets is not required. Simulation and lab experimental results show that the estimated fault resistance has good accuracy with 2% average error. Finally, the method was applied to estimate the fault resistances of more than 50 phase-to-ground faults of a utility company. The ranges of fault resistance values are determined for faults on the company's 240 kV and 138 kV lines.

In this study, a new algorithm for discrimination between internal faults and inrush current conditions in power transformers is presented. First, the proposed algorithm removes the pre-fault conditions of the fault currents. Then, those currents are processed using the discrete wavelet transform to obtain the wavelet matrix. The correlation matrix is computed from wavelet matrix to know the eigenvalues associated with each frequency scale and to reduce the analysed information. This algorithm, which the authors are going to call wavelet correlation modes, is used to identify inrush and internal fault currents in power transformer. To validate the proposed algorithm, simulations are carried out in power systems computer-aided design software, and the obtained signals are analysed in order to evaluate its performance and sensitivity.