An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Fault Detection and <s> The direction of a fault on a power transmission line needs to be identified rapidly and correctly. To classify forward and backward faults on a given line, a neural network's abilities in pattern recognition and classification could be considered as a solution. To demonstrate the applicability of this solution, a neural network technique is employed to design two different fault direction estimators. Details of the design procedure and results of performance studies with the proposed networks are given and compared. <s> BIB001 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Fault Detection and <s> This paper presents a neural network-based technique to identify the direction of a fault on a power transmission line. A novel network using a subset of voltage and current information as its inputs is presented. The network uses samples of voltage and current signals to learn the hidden relationship existing in the input patterns. The network performance is tested with real world fault data. Performance studies results show that the proposed network is able to detect the transmission network fault direction correctly and rapidly. Some of these test results are presented in the paper. <s> BIB002 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Fault Detection and <s> Detection of the direction of a fault on a transmission line is essential to the proper performance of a power system. It would be desirable to develop a high speed and accurate approach to determine the fault direction for different power system conditions. To classify forward and backward faults on a given line, a neural network's abilities in pattern recognition and classification could be considered as a solution. To demonstrate the applicability of this solution, neural network technique is employed and a novel Elman recurrent network is designed and trained. Details of the design procedure and the results of performance studies with the proposed network are given and analysed in the paper. System simulation studies show that the proposed approach is able to detect the direction of a fault on a transmission line rapidly and correctly. It is suitable to realize a very fast transmission line directional comparison protection scheme. <s> BIB003
Step 2: Improving the final estimation of thisdistance using SCALCG based neural network with the high frequency range characteristics. The maximum error of fault location was limited to  5) needs 30% less number of epochs to reduce the error to 5% of its initial value BIB001 and to 15% in BIB002 (i) Authors found that the new 20-input network performed better than the earlier 30-input network (ii) Here, real world fault data had been recorded by Alberta Power Ltd. on the 240 kV transmission systems BIB002 Sanaye-Pasand and Malik (1998) BIB003 Elman network for fault direction estimation 0 ms-12 ms (i) Elman network is a two-layer feed-forward network with the addition of a recurrent connection from the output of the hidden layer to its input (ii) The network outputs which fall above 0.5 and below −0.5 are interpreted as forward and backward faults, respectively (iii) 12 inputs, 12 hidden neurons, and one output neuron (iv) For both hidden and output layers: tansig function 40 different forward and backward faults at the relay location were applied to the system and the network's performance was investigated; in all cases except one 3 phases to ground fault, the directional module performed correctly (v) The parental bias parameter was set to 1.4 (vi) Mutation probability was set to 0.3 and the crossover probability was set to 0.8