An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Disadvantages of GANN: <s> An accurate fault classification algorithm for Teed transmission Circuit based on application of artificial neural networks (ANN) is presented in this paper. The proposed algorithm uses the voltage and current signals of each section measured at one end of teed circuit to detect and classify Double line to ground faults. ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. The adaptive protection scheme based on application of ANN is tested for double line to ground faults, varying fault location, fault resistance and fault inception angle. An improved performance is experienced once the neural network is trained adequately, gives accurate results when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within one cycle; thus the proposed adaptive protection technique is well suited for teed transmission circuit fault detection and classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms. <s> BIB001 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Disadvantages of GANN: <s> This paper presents fault location recognition in transmission power system using artificial neural network (ANN). Single phase short circuit on 110 kV transmission line fed from both ends was analysed with various fault impedances, since it is the most common fault in power system. Load flow and short circuit calculations were performed with EMTP-RV software. Calculation results including currents and voltages at both line ends were used for training ANN in Matlab in order to obtain correct fault location and fault impedance, even for those cases that ANN has never encountered before. The network was trained with back propagation algorithm. Test results show that this approach provides robust and accurate location of faults for a variety of power system operating conditions and gives an accurate fault impedance assessment. <s> BIB002 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Disadvantages of GANN: <s> Abstract To maintain the efficient and reliable operation of power systems, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. A number of mathematical and intelligent techniques are available in the literature for estimating the fault location. However, the results are not satisfactory due to the wide variation in operating conditions such as system loading level, fault inception instance, fault resistance and dc offset and harmonics contents in the transient signal of the faulted transmission line. Keeping in view of aforesaid, a new approach based on generalized neural network (GNN) with wavelet transform is presented for fault location estimation. Wavelet transform is used to extract the features of faulty current signals in terms of standard deviation. Obtained features are used as an input to the GNN model for estimating the location of fault in a given transmission systems. Results obtained from GNN model are compared with ANN and well established mathematical models and found more accurate. <s> BIB003
GANN training is also a time consuming process as there are a number of populations for each weight; but in this study, the GANN training is off-line, so time consumption does not matter as long as it can achieve better classification Average misclassification rate: GANN: 2.35% BPNN: 3.70% Among all the ANN modules, results of RBF network were found to be better than the other two networks in terms of accuracy  Advances in Artificial Neural Systems BIB001 , Yadav et al.  , Teklic et al. BIB002 , and Jamil et al. BIB003 . A comparative study of different ANN based fault detection, classification, and location schemes is given in Table 2 highlighting the methods used, their response time, and ANN features along with remarks.