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In this paper, we propose a novel image indexing and retrieval algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR). The standard local binary pattern (LBP) and local ternary pattern (LTP) encode the relationship between the referenced pixel and its surrounding neighbors by computing gray-level difference. The proposed method encodes the relationship between the referenced pixel and its neighbors, based on the directions that are calculated using the first-order derivatives in vertical and horizontal directions. In addition, we propose a generic strategy to compute nth-order LTrP using (n - 1)th-order horizontal and vertical derivatives for efficient CBIR and analyze the effectiveness of our proposed algorithm by combining it with the Gabor transform. The performance of the proposed method is compared with the LBP, the local derivative patterns, and the LTP based on the results obtained using benchmark image databases viz., Corel 1000 database (DB1), Brodatz texture database (DB2), and MIT VisTex database (DB3). Performance analysis shows that the proposed method improves the retrieval result from 70.34%/44.9% to 75.9%/48.7% in terms of average precision/average recall on database DB1, and from 79.97% to 85.30% and 82.23% to 90.02% in terms of average retrieval rate on databases DB2 and DB3, respectively, as compared with the standard LBP.

Protection of medium- and large-power transformers has always remained an area of interest of relaying engineers. Conventionally, the protection is done making use of magnitude of various frequency components in differential current. A novel technique to distinguish between magnetising inrush and internal fault condition of a power transformer based on the difference in the current wave shape is developed. The proposed differential algorithm makes use of radial basis probabilistic neural network (RBPNN) instead of the conventional harmonic restraint-based differential relaying technique. A comparison of performance between RBPNN and heteroscedastic-type probabilistic neural network (PNN) is made. The optimal smoothing factor of heteroscedastic-type PNN is obtained by particle swarm optimisation technique. The results demonstrate the capability of RBPNN in terms of accuracy with respect to classification of differential current of the power transformer. For the verification of the developed algorithm, relaying signals for various operating conditions of the transformer, including internal faults and external faults, were obtained through PSCAD/EMTDC. The proposed algorithm has been implemented in MATLAB. [ABSTRACT FROM AUTHOR]

This article presents development of a new percentage differential protection scheme for double-circuit transmission line using wavelet transform (WT). The work presented addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines sharing the same right of way. By the use of powerful analyzing and decomposing features of WT, a new technique is presented that takes care of the problems faced by pilot independent distance relay. The suggested technique depends on the six line currents of the two parallel lines at each end. The idea of the protection algorithm is based on the percentage of the difference of the two line current of each corresponding phase at each terminal. The measured six line currents at the relay location are decomposed into three levels of decomposition using the db4 mother wavelet. The summation of third level approximation coefficients are used as bias current whereas third level detailed coefficients are used as differential current. The proposed method eliminates many of the problems associated with double-circuit lines, such as high fault resistance, cross-country fault, mutual coupling effects, remote infeed, and avoids the problem of single-end supply. The stability of the relay under varied system configurations and operating conditions is examined. The relay algorithm is tested for various probable fault inception angles and loading conditions and with varying fault resistance between 0-150 ohms. The proposed wavelet based scheme provides encouraging results for different fault and system conditions. Relaying signals under different fault conditions were obtained by simulating the system using PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

Investigations towards the applicability of probabilistic neural networks (PNNs) as core classifiers to discriminate between magnetising inrush and internal fault of power transformer are made. An algorithm has been developed around the theme of conventional differential protection of transformer. It makes use of the ratio of the voltage-to-frequency and the amplitude of differential current for the detection of the operating condition of the transformer. The PNN has a significant advantage in terms of a much faster learning capability because it is constructed with a single pass of exemplar pattern set and without any iteration for weight adaptation. For the evaluation of the developed algorithm, transformer modelling and simulation of fault are carried out in power system computer-aided designing PSCAD/EMTDC. The operating condition detection algorithm is implemented in MATLAB. [ABSTRACT FROM AUTHOR]

The distance calculation performed by a distance relay is incorrect due to ground fault resistance, prefault system conditions, mutual coupling effect and shunt capacitance influences. The problem is more serious keeping in view that all relay settings are made based on some compromise. The work presented in this article addresses the problems encountered by conventional pilot independent distance relay when protecting two terminal transmission lines. The key feature is that it presents the detailed analysis of the apparent impedance as seen from the relaying point taking into account the effects of transmission line parameter uncertainties, shunt capacitance influences and variations in the system external to the protected line. These results can be used for estimation of correct impedance of transmission line, hence taking relaying decisions. Based on the results of extensive computer simulation of the infeed/outfeed, fault resistance and shunt capacitance effects on the relay characteristics, an adaptive distance relaying scheme is proposed for such lines using a radial basis function neural network (RBFNN), which provides a more efficient approach for training, computation, adaptation, and tripping than the conventional feed forward network using back propagation algorithm (BPNN). The proposed adaptive protection scheme is tested for a single line to ground fault, but for varying fault locations, fault resistances, fault inception angles, and different source impedance ratio too. At the end, comparison of conventional feed forward network using back propagation algorithm with radial basis function classifier is also given, which clearly indicates improvement. Hence, the selectivity of the protection system is increased, as is the power system reliability. [ABSTRACT FROM AUTHOR]