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Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.5, No. 4)

Publication Date:

Authors : ;

Page : 43-50

Keywords : Bagasse Ash; concrete; Crimped steel fiber; partial replacement; Compressive strength; flexural;

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The neural network represents a network with a finite number of layers consisting of solitary elements that are similar to neurons with different types of connection among layers. The number of layers is desired to be minimal in order to decrease the problem solving time. Basically, we can design and train the neura l networks (NN) for solving particular problems which are difficult to solve through the human beings or the conventional computational method. The computational of the training comes down to the adjustments of certain weights which are the key elements of the Artificial Neural Network. This is one of the key differences of the NN (neural network) approach to problem solving than conventional computational method. This adjustment of the weights takes place when the NN is presented with the input data record s and the corresponding target values. In the possibility of neural network training with off - line data, they are found useful for power system. The neural network (NN) applications in transmission line protection are mainly concerned with in improvements in achieving more effective and efficient fault diagnosis and distance relaying. NN application can be used for overhead transmission lines, as well as in power distribution systems. Back propagation neural network approach is studied and implemented. The voltage and current signals of the line are observed to perform these three tasks. The detailed coefficients of all phase current that are collected only at the sending end of a transmission line are selected as parameters for fault detection classificatio n.

Last modified: 2016-04-05 22:56:04