An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Artificial Neural Network <s> Fault section estimation is of great importance to the restoration of power systems. In this paper, the application of Radial Basis Function Neural Network (RBF NN) to fault section estimation is addressed. The orthogonal least square algorithm has been extended to optimize the parameters of RBF NN. A classical Back Propagation Neural Network (BP NN) has been developed to solve the same problem for comparison. Computer test is conducted on a 4 bus test system and the test results show that the RBF NN is quite effective and superior to BP NN in fault section estimation. <s> BIB001 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Artificial Neural Network <s> Summary form only given. This paper presents a neural system intended to aid the control center operator in the task of fault section estimation. Its analysis is based on information about the operation of protection devices and circuit breakers. In order to allow the diagnosis task, the protection system philosophy of busbars, transmission lines and transformers are modeled with the use of two types of neural networks: the general regression neural network (GRNN) and the multilayer perceptron neural network (MLP). The tool described in this paper can be applied to real bulk power systems and is able to deal with topological changes, without having to retrain the neural networks. <s> BIB002 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Artificial Neural Network <s> At present quality of software systems is a major issue, still well defined criteria to measure it needs to be established. The object-oriented (OO) systems, which is different from procedural paradigm requires valid and effective metrics to assess quality of the software. There is considerable research interest in developing and applying sophisticated techniques to construct models for estimation. The different soft computing techniques such as Artificial Neural Networks (ANN), fuzzy inference systems and adaptive neuro-fuzzy inference systems are available for the prediction purpose. However among these techniques, ANN possesses the advantages of predicting software quality accurately and identifies the defects by efficient discovery mechanisms. This paper aims to survey various research methodologies proposed to predict quality of OO metrics by using neural network approach. <s> BIB003 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Artificial Neural Network <s> To accomplish software quality, correct estimation of maintainability is essential. However there is a complex and non-linear relationship between object-oriented metrics and maintainability. Thus maintainability of object-oriented software can be predicted by applying sophisticated modeling techniques like artificial neural network. Multilayer Perceptron neural network is chosen for the present study because of its robustness and adaptability. This paper presents the prediction of maintainability by using a Multilayer Perceptron (MLP) model and compares the results of this investigation with other models described earlier. It is found that efficacy of MLP model is much better than both Ward and GRNN network models. <s> BIB004
Artificial neural network (ANN) has been equipped with distinctiveness of parallel processing, nonlinear mapping, associative memory, and offline and online learning abilities. The wide uses of ANN with its conquering outcomes make it an effective diagnostic mean in electric power systems. Its versatility with multitude applicability can be seen in other areas of science and engineering research BIB003 BIB004  . It is a complex network of interconnected neurons where firing of electrical pulses via its connections leads to information propagation. ANN is trained by using prior chosen fault samples as input and set of fault information as output for fault diagnosis application. Neural networks are comprised of primarily three basic learning algorithms such as supervised learning, unsupervised learning, and reinforced learning. Among these supervised learning is most commonly used and is also referred to as learning with a teacher. This is applied when the target is having identified value and is associated with each input in the training set BIB003 . Figure 1 represents the supervised architecture of ANN. Error back propagation (BP) neural network was applied by Chan  for diagnosis of fault in power system. However slow speed training and the shortcomings of local optima lead to the introduction of additional momentum factor for problem solving. Radial basis function (RBF) neural network has a faster learning speed and the ability of arbitrary function approximation. Bi et al. presented a novel RBF neural network for estimating section of fault. Their simulation results of 4-bus test system shown that the capability of RBF neural network in grid fault diagnosis was better than the conventional BP neural net BIB001 . For solving improper problems, neural network topologies are to be altered and there is a need to retrain the network. Cardoso et al. BIB002 used the true capacity of multilayer perception (MLP) and generalized regression neural network (GRNN) for fault estimation in electric power system. GRNN is having the advantage of faster learning, global optimum, and lower requirement of comprehensive sample. They fed the failure information into MLP and the resultant outcome was given as output to GRNN. They also compared ANN fault diagnosis methods with expert system diagnostic methods and found that ANN based methods may evade the formation of expertise, expert heuristic knowledge, and expression and hence save tedious work.