An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Faulty <s> This paper is concerned with a new approach for fault type classification and faulted phase selection based on artificial neural networks (ANN) to be used for power transmission line protection. The proposed approach is based on a 2-level hierarchical neural network structure. Compared to other architectures, this structure would have a high learning ability and accordingly higher recall accuracy. To reach the corresponding decision, the normalized changes from prefault condition in the instantaneous phase voltages and currents at the relaying point are used. This would lead to an inherent adaptive feature of the approach. <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 "Faulty <s> A novel application of neural network approach to protection of double circuit transmission line is demonstrated in this paper. Different system faults on a protected transmission line should be detected and classified rapidly and correctly. The proposed method uses current signals to learn the hidden relationship in the input patterns. Using the proposed approach, fault detection, classification and faulted phase selection could be achieved within a quarter of cycle. An improved performance is experienced once the neural network is trained sufficiently and suitably, thus performing correctly when faced with different system parameters and conditions. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms. <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 "Faulty <s> A new approach for distance relaying of transmission line using machine intelligence technique such as support vector machine (SVM) is presented. The proposed SVM technique is used for faulty phase selection and ground detection in different fault situations that occur on large power transmission line. Post-fault current and voltage samples for one-fourth cycle (five samples) are used as inputs to SVM 1, which provide output for faulty phase selection. SVM 2 is trained and tested with zero-sequence components of fundamental, third and fifth harmonic components of the post-fault current signal to provides the involvement of ground in the fault process. The polynomial and Gaussian kernel SVMs are designed to provide the most optimised boundary for classification. The total time taken for faulty phase selection and ground detection is 10 ms (half cycle) from the inception of fault. Also the proposed technique is tested on experimental set-up with different fault situations. The test results are compared with those of the radial basis function neural network and were found to be superior with respect to efficiency and speed. The classification test results from SVMs are accurate for simulation model and experimental set-up, and thus provide fast and robust protection scheme for distance relaying in transmission line. <s> BIB003 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Faulty <s> Modern numerical relays often incorporate the logic for combined single and three-phase auto-reclosing scheme; single phase to earth faults initiate single-phase tripping and reclosure, and all the other faults initiate three-phase tripping and reclosure. Accurate faulted phase selection is required for such a scheme. This paper presents a novel scheme for detection and classification of faults on double circuit transmission line. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the faulted phase. MATLAB/Simulink software is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed phase selector scheme is able to identify faulted phase on the double circuit transmission line rapidly and correctly. <s> BIB004 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Faulty <s> This pape presents artificial neutral network algorithm for fault type classification and fault distance location for all the types of faults for transmission line in triangle network. This technology uses only one end data and accurate fault distance location fault type identifying is achieved after half cycle from the inception of fault. The proposed Artificial Neutral Network (ANN) based fault distance locator and fault distance locator uses transient energy which are extracted by S-transform. An improved performance is obtained once the neutral network is trained suitably, thus performance correctly when faced with different system parameters and conditions i.e. varying fault type, fault location, fault resistance, fault inception angle. <s> BIB005 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Faulty <s> The proposed work deals with Fault classification a fault location techniques for parallel overhead transmission lines. Fault location is carried out by measuring the distributed line model of faulted line parameters. Different system faults such as LG, LLG and LLLG on a protected transmission line should be detected, classified a located rapidly in order to bring the system to the normal state. A novel application of neural network approach with three variance of feed forward neural networks such as the one with Back propagation algorithm (BPN), Radial basis function (RBF) network and Cascaded correlation feed forward network (CFBPN) is proposed for the protection of double circuit transmission line has been demonstrated in this work. The proposed method uses line current values to learn the hidden relationship in the input patterns. Using the proposed approach, fault detection, classification, location and faulted phase selection could be achieved. An improved performance is experienced once the neural network is trained sufficiently and suitably, thus performing correctly when faced with varied system parameters and conditions. Results of performance studies show that the proposed neural network-based modules outperform the performance of conventional fault selection algorithms. Among the ANN modules, result of RBF network is found to be better than the other two networks in terms of accuracy. <s> BIB006
Phase Selection". Fault phase selection, an imperative part of fault diagnosis, is carried out by measuring faulty line parameters. Different power system faults such as LG, LL, LLG, LLL, and LLLG on a protected transmission line should be detected, classified, and located and faulty phase should be selected swiftly for performing the normal system operation. The summarized study of different ANN based fault phase selection schemes is given in Table 4 highlighting the methods used, their response time, and ANN features along with remarks BIB001  BIB002  BIB003 BIB004 BIB005  BIB006 .