The frequent power failure observed in the transmission network is caused by, increase in fault classification such as single-line-to-ground, double-line-to-ground, or three-phase faults. And decrease in the identification of faults. This is overcome by introducing Fault diagnosis on a power system transmission line using neural network. To vividely achieve this, it is done in this manner, characterizing Fault diagnosis on a power system transmission line, designing a conventional SIMULINK model for Fault diagnosis on a power system transmission line, training ANN in the identified Fault diagnosis on a power system transmission line for immediate reduction and enhancement of the network performance, developing an algorithm that will implement the process, designing a SIMULINK model for Fault diagnosis on a power system transmission line using neural network and validating and justifying percentage improvement in the reduction of faults on a power system transmission line with and without neural network. The results obtained are the conventional single-line-to-ground in Fault diagnosis on a power system transmission line is 76% while when neural network is introduced in the system, it decreases to72.46%. The percentage improvement in the reduction of single-line-to-ground fault when ANN is introduced in the system is 3.54% and the conventional identification of fault in Fault diagnosis on a power system transmission line is 81% while when neural network is introduced in the system, it increases to77.23%. The percentage improvement in the improvement of identification of fault when ANN is introduced in the system is 3.77%.
Nwogbu et al. (Thu,) studied this question.