An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> A method for the computation of fault location in two- and three-terminal high voltage lines is presented. It is based on digital computation of the three-phase current and voltage 60/50 Hz phasors at the line terminals. The method is independent of fault type and insensitive to source impedance variation or fault resistance. Furthermore, it considers the synchronization errors in sampling the current and voltage waveforms at the different line terminals. The method can be used online following the operation of digital relays or offline using data transferred to a central processor from digital transient recording apparatus. The authors start with a two-terminal line to explain the principles and then present the technique for a three-terminal line. The technique was first tested using data obtained from a steady-state fault analysis program to evaluate the convergence, observability, and uniqueness of the solution. The technique was then tested using EMPT-generated transient data. The test results show the high accuracy of the technique. > <s> BIB001 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> Adaptive relaying has the validity for a wide variety of applications. Here a typical problem of maloperation is considered. The application of the modified multilayer perceptron (MLP) mode can successfully avoid the maloperation of a relay. For the cases considered, it shows encouraging results. The advantage associated with the presented MLP model is that the modified characteristic can be defined in the absence of a definite analytical model since the artificial neural network (ANN) can learn it through input-output patterns. The methodology can be extended to many adaptive protective schemes. This report just opens new vistas for the exploration of the application of ANNs in adaptive protective schemes, and further investigations could lead to increased confidence. > <s> BIB002 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> All relay settings are a compromise. Adaptive relaying accepts that relays that protect a power system may need to change their characteristics to suit the prevailing power system conditions. This philosophy has a wide range of applications covering many protective schemes. Here we consider a two-terminal transmission line, confirm that fault resistance and the location of faults can produce erroneous relay function and finally suggest ways to ensure the generation of the correct signal for relay operation. Retaining the microprocessor based framework, we show how artificial neural networks (ANNs) can be used effectively to achieve adaptive relaying for the above-mentioned problem. Adaptive relaying covers a large number of applications and the characteristics of relays vary widely, so the philosophy of adaptive relaying must vary accordingly. A modified multilayered perceptron model employs an additional node in the input layer. This additional input facilitates changes in the relay characteristic. The desired change in the quadrilateral relay characteristic is achieved by making appropriate changes in the thresholds and weights of the hidden layer neurons. A multiparameter adaptive scheme assumes that the additional input of the phase angle is available. Simulation results using ANNs for the different applications of adaptive relaying mentioned above are presented and discussed. <s> BIB003 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> This paper describes the application of an artificial neural network-based algorithm to the single-ended fault location of transmission lines using voltage and current data. From the fault location equations, similar to the conventional approach, this method selects phasors of prefault and superimposed voltages and currents from all phases of the transmission line as inputs of the artificial neural network. The outputs of the neural network are the fault position and the fault resistance. With its function approximation ability, the neural network is trained to map the nonlinear relationship existing in the fault location equations with the distributed parameter line model. It can get both fast speed and high accuracy. The influence of the remote-end infeed on neural network structure is studied. A comparison with the conventional method has been done. It is shown that the neural network-based method can adapt itself to big variations of source impedances at the remote terminal. Finally, when the remote source impedances vary in small ranges, the structure of the artificial neural network has been optimized by the pruning method. <s> BIB004 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> A distance relay for the protection of transmission lines is usually designed on the basis of fixed settings. The reach of such relays is therefore affected by the changing network conditions. The implementation of a pattern recognizer for power system diagnosis can provide great advances in the protection field. This paper demonstrates the use of an artificial neural network as a pattern classifier for a distance relay operation. The scheme utilizes the magnitudes of three phase voltage and current phasors as inputs. An improved performance with the use of an artificial neural network approach is experienced once the relay can operate correctly, keeping the reach when faced with different fault conditions as well as network configuration changes. <s> BIB005 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> This paper presents an artificial neural network (ANN) based approach to improve the speed of a differential equation based distance relaying algorithm. As the differential equation used for the transmission line protection is valid only at low frequencies, the distance relaying algorithm requires a lowpass filter, removing frequency components higher than those for relaying. However, the lowpass filter causes the time delay of the components for relaying. Thus, the calculated resistances and reactances do not converge directly to the fault distance even after data window occupies post fault data. Faults with the same fault inception angle have similar shapes of impedance loci. If an ANN is trained with the shape of various impedance loci for fault distances and fault inception angles, it can predict the fault distance with some values of calculated resistances and reactances before they converge to the fault distance. Therefore, the ANN can improve the speed of the distance relaying algorithm without affecting its accuracy. Moreover, the proposed approach can speed up more when a higher sampling rate is employed. The proposed approach was tested in three rates of 24, 48 and 96 samples/cycle (s/c) in a 345 (kV) transmission system and compared with the conventional distance relaying algorithm without ANNs from the speed and accuracy viewpoints. As a result, the approach can improve the speed of the relaying algorithm. <s> BIB006 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> The paper presents a new approach for classification and location of faults on a transmission line using a newer version of radial basis function neural network (RBFNN) which provides a more efficient approach for training and computation. The input data to the RBFNN comprise the normalised peak values of the fundamental power system voltage and current waveforms at the relaying location obtained during fault conditions. The extraction of the peak components is carried out using an extended Kalman filter (EKF) suitably modelled to include decaying d.c., third and fifth harmonics along with the fundamental. The fault training patterns required using the efficient version of RBF neural network are much less in comparison to the conventional RBF network and the choice of neurons and the parameters of the network are systematically arrived without resorting to trial and error calculations. The new approach provides a robust classification of different fault types for a variety of power system operating conditions with resistance in the fault path. Further a new fault location strategy is formulated using four neural networks, one each for the major category of faults like LG, LL, LLG and LLL faults. The proper feature selection for the networks results in an accurate and fast distance relaying scheme. © 2001 Published by Elsevier Science B.V. <s> BIB007 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> Abstract A novel feedforward neural network that indicates whether a fault is within or outside the protection zone (fault indicator) of a transmission line is presented. The same technique has been extended to locate the distance of the fault (fault locator). The proposed scheme utilizes the frequency spectrum of the voltages and currents to make a decision. Data obtained from the electromagnetic transient program for single-line-to-ground faults and three-phase faults have been tested and the results are found to be accurate. Using this neural network the relaying decision is obtained within a cycle after the fault inception. The performance of the trained neural network shows promise and has the potential for implementation in a digital relay for transmission line protection. <s> BIB008 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> Recent developments indicate that Artificial Neural Networks (ANNs) may be appropriate for assisting dispatchers in operating electric power systems. The fault location algorithm being a key element in the digital relay for power transmission line protection, this paper discusses the potential applicability of ANN techniques for determination of fault location and fault resistance on EHV transmission lines with remote end in-feed. Most of the applications make use of the conventional Multi Layer Perceptron (MLP) model based on back propagation algorithm. However, this model suffers from the problem of slow learning rate. A modified ANN learning technique for fault location and fault resistance determination is presented in this paper. A reasonably small NN is built automatically without guessing the size, depth, and connectivity pattern of the NN in advance. Results of study on a 400 kV transmission line are presented for illustration purposes. Performance of the modified ANN is compared with the analytical algorithms and conventional MLP algorithm for different combinations of pre-fault loading condition, fault resistance and fault location. The results are found to be encouraging. <s> BIB009 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> A system is proposed to locate faults on transmission lines using the traveling wave phenomenon. Prony method is used to analyze the voltage or current signal at the local bus and extract its modal information. This information represents the traveling wave generated by the fault and can be used to estimate its location. Artificial neural networks (ANNs) are used to estimate the fault location based on the modal information. Two schemes are presented: the first with a voltage input signal; and the second with a current input signal. Training and testing data are obtained by ATP-EMTP simulations. Tests were done to study the effect of fault resistance variation and fault incidence angle on both schemes. The system has the advantages of utilizing single-end measurements, using either a voltage or current signal; no pre-fault data is required. <s> BIB010 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> A real-time fault detector for the distance protection application, based on artificial neural networks, is described. Previous researchers in this field report use of complex filters and artificial neural networks with large structure or long training times. An optimum neural network structure with a short training time is presented. Hardware implementation of the neural network is addressed with a view to improve the performance in terms of speed of operation. By having a smaller network structure the hardware complexity of implementation reduces considerably. Two preprocessors are described for the distance protection application which enhance the training performance of the artificial neural network many fold. The preprocessors also enable real-time functioning of the artificial neural network for the distance protection application. Design of an object oriented software simulator, which was developed to identify the hardware complexity of implementation, and the results of the analysis are discussed. The hardware implementation aspects of the preprocessors and of the neural network are briefly discussed. <s> BIB011 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> The paper presents a new approach for the protection of power transmission lines using a minimal radial basis function neural network (MRBFNN). This type of RBF neural network uses a sequential learning procedure to determine the optimum number of neurons in the hidden layer without resorting to trial and error. The input data to this network comprises fundamental peak values of relaying point voltage and current signals, the zero-sequence component of current and system operating frequency. These input variables are obtained by a Kalman filtering approach. Further, the parameters of the network are adjusted using a variant of extended Kalman filter known as locally iterated Kalman filter to produce better accuracy in the output for harmonics, DC offset and noise in the input data. The number of training patterns and the training time are drastically reduced and significant accuracy is achieved in different types of fault classification and location in transmission lines using computer simulated tests. <s> BIB012 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> This paper presents an artificial neural network (ANN) based approach for three-zone distance protection of transmission lines. The proposed neural network-based distance relay has multilayer feedforward architecture with two inputs and three trip/(no trip) output signals. The first output is responsible for main protection of the transmission line section, whereas the other two outputs provide back-up protection for the adjacent line sections. The input features of the neural network are the fundamental frequency voltage and current magnitudes extracted by discrete-Fourier transform. In this paper, the backpropagation training technique has been used for off-line training of the proposed ANN distance relay. The input-output patterns were simulated for faults covering the three zones of protection at different locations, operating conditions, and fault inception angles. The simulation results presented in this paper show that the proposed ANN distance relay is very effective in detection and classification of line faults and therefore can be considered as a good tool for main and backup digital distance protection. <s> BIB013 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> In this paper, a transmission line fault location model which is based on an Elman recurrent network (ERN) has been presented for balanced and unbalanced short circuit faults. All fault situations with different inception times are implemented on a 380-kV prototype power system. Wavelet transform (WT) is used for selecting distinctive features about the faulty signals. The system has the advantages of utilizing single-end measurements, using both voltage and current signals. ERN is able to determine the fault location occurred on transmission line rapidly and correctly as an important alternative to standard feedforward back propagation networks (FFNs) and radial basis functions (RBFs) neural networks. <s> BIB014 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> This work presents the development and implementation of an artificial neural network based algorithm for transmission lines distance protection. This algorithm was developed to be used in any transmission line regardless of its configuration or voltage level. The described ANN-based algorithm does not need any topology adaptation or ANN parameters adjustment when applied to different electrical systems. This feature makes this solution unique since all ANN-based solutions presented until now were developed for particular transmission lines, which means that those solutions cannot be implemented in commercial relays. <s> BIB015 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> Distance relays are widely used for protection of transmission lines. Traditionally used electromechanical distance relays for protection of transmission lines are prone to effects of fault resistance. Each fault condition corresponds to a particular pattern. So use of a pattern recognizer can improve the relay performance. This paper presents a new approach, known as artificial neural network (ANN) to overcome the effect of fault resistance on relay mal-operation. In this paper effect of fault resistance for single line to ground type of fault is considered. The scheme utilizes the magnitudes of resistance and reactance as inputs. Once trained with a large number of patterns corresponding to various conditions, it can classify unknown patterns. <s> BIB016 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Distance Protection by ANN <s> This article presents a new methodology for transmission line protection that uses higher-order statistics (HOS), cumulants, and artificial neural networks (ANNs). The main objective is to design a distance relay algorithm. Results for the fault-detection and -classification stages are presented, as well as for fault location. The proposed method combines a large number of samples of cumulants with different features and the capability of ANNs to discriminate different patterns. In summary, HOS are used for feature extraction in the protection scheme. The ANNs receive these statistics as inputs, and they are responsible for the logical functioning of the protection system, deciding if a trip is needed after detecting, classifying, and locating a fault. The results have shown that the proposed approach is suitable for protection purposes. For the fault-detection stage, results have shown to be immune to the high presence of additive noise and also to the power-system frequency deviation. Moreover, the fault-classification stage is computed without the need of current information from the power system. Finally, the preliminary results for fault location are precise for a correct estimation of fault distance and determination of the fault zone. It must be highlighted that this new distance protection approach is essentially based on voltage signals, using current signals only for determining the direction of the fault. This fact represents an innovation in distance relaying. <s> BIB017
The fundamental principle of distance protection is that the apparent impedance seen by the relay reduces considerably in case of line fault. A fault is indicated if the ratio of apparent impedance to the positive sequence impedance is less than Advances in Artificial Neural Systems 3 unity. This scheme of protection is inherently directional and used by impedance and Mho relays. This paper focuses upon the studies of distance protection scheme applying ANN approach. Adaptive relaying was introduced for widespread applications including incorrect or fault operations measurement. The learning capacity of ANN from input and output patterns extended its applicability in several adaptive protection schemes. Khaparde et al.  applied adaline neural network model in offline mode for protective relaying operation of transmission lines. They also proposed adaptive distance protection by using ANN BIB002 . They have applied MLP model to reduce misoperation of a relay. Girgis et al. BIB001 presented a method for the computation of fault location in twoand three-terminal high voltage lines which is based on digital computation of the three-phase current and voltage 60/50 Hz phasors at the line terminals. For evaluation of the convergence and distinctive solution, this method was tested by electromagnetic transient (EMPT) generated transient data from a steady state fault analysis. Qi et al.  proposed ANN approach for distance protection of power system by taking trained data from simulation of a simple power system under load and fault conditions. According to them conventional distance relays might not function properly under certain conditions such as nonlinear arc resistance, high impedance fault, and variable source impedance. However if such relays are implemented with ANN, such issues can be addressed. Khaparde BIB003 again proposed an adaptive scheme of distance protection using an artificial neural network. Lai  implemented an adaptive protection scheme by ANN approach for classification purpose. They have considered conditions of high impedance fault (hard detection because of minute fault current) and variable source impedance. Coury and Jorge BIB005 proposed distance protection using ANN for transmission lines utilizing the magnitudes of three-phase voltage and current phasors as inputs. ANN based approach for improving the speed of a differential equation based distance relaying algorithm was developed by Cho et al. BIB006 . Several researchers illustrated various methodologies for improvements in fault distance computation BIB008 BIB004 BIB009 BIB010 BIB014 . Venkatesan and Balamurugan BIB011 developed neural network simulator for identifying the optimum ANN structure necessary to train the data and implement the ANN in hardware. However there is no precise rule for selection of the number of hidden layers and neurons per hidden layer. So it is not certain whether or not the ANN based relay gives the optimum output, for maintaining the integrity of the boundaries of the relay characteristics. Pradhan et al. BIB007 proposed a high speed distance relaying scheme based on RBF neural network due to its capability of distinguishing faults with data falling outside the training pattern. A sequential procedure for distance protection using a minimal RBF neural network for determining the optimum number of neurons in the hidden layer without resorting to trial and error was illustrated by Dash et al. BIB012 . Authors BIB013 trained multilayer feed-forward architecture with two inputs and three-trip or no-trip output signals based approach and used BP technique for three-zone distance protection of transmission lines. The first output was used for main protection of the transmission line section, whereas the other two outputs provide backup protection for the adjacent line sections. The input features extracted by discrete-Fourier transform from the fundamental frequency voltage and current magnitudes. Santos and Senger BIB015 developed and implemented of a unique ANN based algorithm for transmission lines distance protection. Their algorithm can be used in any transmission line despite of its configuration or voltage level and also does not require any topology adaptation or parameters adjustment when applied to varied electrical systems. Vaidya and Venikar BIB016 illustrated an ANN based distance protection scheme for long transmission lines by considering the effect of fault resistance of single line to ground fault type. They have utilized the magnitudes of resistance and reactance as inputs for classifying unknown patterns. A novel distance protection approach for detection and classification stages based on cumulants and neural networks was developed by Carvalho et al. BIB017 .