An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination
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paper_title: A Novel Neural Network Approach for Fault Section Estimation

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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.
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paper_title: Maintainability prediction of object-oriented software system by multilayer perceptron model

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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.
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paper_title: Quality Prediction in Object Oriented System by Using ANN: A Brief Survey

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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.
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paper_title: Application of neural network modules to electric power system fault section estimation

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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.
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paper_title: An adaptive approach in distance protection using an artificial neural network

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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.
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paper_title: Artificial neural network approach to single-ended fault locator for transmission lines

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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.
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paper_title: A fast and accurate distance relaying scheme using an efficient radial basis function neural

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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.
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paper_title: A real-time hardware fault detector using an artificial neural network for distance protection

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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.
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paper_title: Artificial neural network approach to distance protection of transmission lines

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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.
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paper_title: A transmission line fault locator based on Elman recurrent networks

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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.
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paper_title: High-speed transmission line relaying using artificial neural networks

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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.
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paper_title: ANN-based techniques for estimating fault location on transmission lines using Prony method

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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.
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paper_title: An ANN based approach to improve the speed of a differential equation based distance relaying algorithm

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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.
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paper_title: ANN based distance protection of long transmission lines by considering the effect of fault resistance

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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.
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paper_title: Adaptive relaying using artificial neural network

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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. >
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paper_title: Application of minimal radial basis function neural network to distance protection

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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.
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paper_title: A new fault location technique for two- and three-terminal lines

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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. >
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paper_title: Transmission lines distance protection using artificial neural networks

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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.
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paper_title: ANN applications in fault locators

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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.
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paper_title: A new approach for distance protection using artificial neural network

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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.
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paper_title: A New Transmission Line Protection Approach Using Cumulants and Artificial Neural Networks

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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.
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paper_title: Neural-based monitoring system for health assessment of electric transmission lines

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This paper presents a real-time monitoring system based on artificial neural network (ANN) to diagnose mechanical integrity of electric transmission lines. A sensor system composed of a receiver and a transmitter generates micro-acoustic waves along the conductor, and captures reflected waves. For key information, embedded in the captured signatures, feature extraction methods and ANN-based classifiers are considered and employed. In this monitoring system, fault detection is achieved using the reflected signatures from broken strands of transmission lines. Details of the system design, procedure and the results of performance studies in a laboratory testbed are presented. Based on laboratory experimental results, this system with both MLP (multilayer perceptron) and ART (adaptive resonance theory) classifiers show satisfactory performance in fault classification.
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paper_title: Performance evaluation of a new transmission line directional module using field data

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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.
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paper_title: High speed transmission system directional protection using an Elman network

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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.
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paper_title: Power transmission lines fault direction estimation using artificial neural networks

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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.
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paper_title: Artificial neural network approach for locating faults in power transmission system

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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.
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paper_title: Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

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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.
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paper_title: Fault Classification and Faulty Section Identification in Teed Transmission Circuits Using ANN

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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.
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paper_title: Genetic algorithm based neural networks applied to fault classification for EHV transmission lines with a UPFC

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The paper proposes a novel fault detection and classification scheme for EHV power transmission lines using genetic algorithm-based neural networks. The application concerned is fault classification for EHV lines with a unified power factor corrector (UPFC), since fault classification is a key part of protective relaying schemes. After the genetic algorithm-based neural network is briefly discussed in general, EMTP based digital simulation results of a UPFC transmission system are presented. The generation of training/test data and preprocessing of these data for neural networks are then described. The paper places special emphasis on the performance comparison between a genetic algorithm-based neural network and a backpropagation network-based scheme.
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paper_title: Design, implementation and testing of an artificial neural network based fault direction discriminator for protecting transmission lines

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This paper describes a fault direction discriminator that uses an artificial neural network (ANN) for protecting transmission lines. The discriminator uses various attributes to reach a decision and tends to emulate the conventional pattern classification problem. An equation of the boundary describing the classification is embedded in the multilayer feedforward neural network (MFNN) by training through the use of an appropriate learning algorithm and suitable training data. The discriminator uses instantaneous values of the line voltages and line currents to make decisions. Results showing the performance of the ANN-based discriminator are presented in the paper and indicate that it is fast, robust and accurate. It is suitable for realizing an ultrafast directional comparison protection of transmission lines. >
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paper_title: Modular neural network-based directional relay for transmission line protection

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This letter proposes the application of a modular neural network as a mechanism to discriminate the direction of faults for transmission line protection. The modular neural network approach solves a relatively complex problem by decomposing it into simpler subtasks that are easier to manage and then assembles the solution from the results of the subtasks. In addition to the obvious advantages of a neural network as a fault classifier, incorporation of modularity to the network structure provides more important positive attributes like model complexity reduction, better learning capability, etc. The modular neural network concept has been utilized successfully to develop a directional relay algorithm for a transmission system and subsequently implemented on a DSP TMS320F243 EVM-board.
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paper_title: Transmission line fault distance and direction estimation using artificial neural network

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An accurate fault distance and direction estimation based on application of artificial neural networks for protection of doubly fed transmission lines is presented in this paper. The proposed method uses the voltage and current available at only the local end of line. This method is adaptive to the variation of fault location, fault inception angle and fault resistance. The Simulation results show that single phase-to-ground faults (both forward and reverse) can be correctly detected and located after one cycle after the inception of fault. Large number of fault simulations using MATLAB® has proved the accuracy and effectiveness of the proposed algorithm. The proposed scheme has significant advantage over more traditional direction relaying algorithms viz. it is suitable for high resistance fault. It has the operating time of less than 1.5 cycles. The proposed scheme allows the protection engineers to increase the reach setting up to 90% of the line length i.e. greater portion of line length can be protected as compared to earlier techniques in which the reach setting is 80-85% only.
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paper_title: Performance evaluation of a new transmission line directional module using field data

paper_content:

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.
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paper_title: High speed transmission system directional protection using an Elman network

paper_content:

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.
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paper_title: Improved first zone reach setting of artificial neural network-based directional relay for protection of double circuit transmission lines

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This study proposes an artificial neural network-based protection scheme for double circuit transmission line with improved first zone reach setting up to 99% of line length. The proposed scheme involves three stages. The first stage makes the discrimination among normal condition and faults. The second stage identifies the zone/section of the fault from the relay location. If a forward fault is detected in its first zone then the third stage is activated, which classifies the fault type and identifies the faulty phase. The three-phase currents and voltages measured at only one end of the double circuit line are used to calculate discreet Fourier coefficients. Thus, this technique does not require any communication link. The algorithm is presented in detail and extensively tested using the simulink model of a 400 kV, 300 km distributed parameter line simulated in MATLAB® 2009a in the time domain. The simulation results show that all types of shunt faults (forward as well as reverse), its zone/section and faulty phase can be correctly identified within a half cycle time. This method is adaptive to the variation of fault type, fault inception angle, fault location, fault resistance, single circuit operation and CT saturation. The main advantage of the proposed scheme is that it offers primary protection to total line length using single end data only and back up protection for the adjacent forward and reverse line section also.
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paper_title: Neural network application in directional comparison carrier protection of EHV transmission lines

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The increasing scale and complexity of power systems require high performance and high reliability of power system protection. Protective relaying based on directional comparison with power line carrier or microwave channels is the most suitable protection scheme for long distance EHV transmission lines and widely used in power systems. The key element of such protection is the directional relay used to discriminate fault direction. In order to overcome the disadvantages of conventional directional relays, the authors of this paper put forward a directional comparison carrier protection scheme based on artificial neural networks (ANNs). The protection is extensively tested using the electromagnetic transient program (EMTP) under various electric power system operating and fault conditions. It is proved that the directional comparison carrier protection based on ANNs, which can recognize various fault patterns of the protected transmission line (such as fault direction, fault phases, etc.) correctly under any kind of operating and fault conditions and the whole process, is satisfactory for EHV transmission line protection.
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paper_title: Power transmission lines fault direction estimation using artificial neural networks

paper_content:

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.
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paper_title: Transmission line distance relaying using machine intelligence technique

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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.
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paper_title: Fault phase selection and distance location based on ANN and S-transform for transmission line in triangle network

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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.
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paper_title: A neural-network-based approach for fault classification and faulted phase selection

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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.
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paper_title: Faulted phase selection on double circuit transmission line using wavelet transform and neural network

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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.
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paper_title: A Comparitive Study on ANN Based Fault Location and Classification Technique for Double Circuit Transmission Line

paper_content:

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.
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paper_title: Artificial neural network approach to fault classification for double circuit transmission lines

paper_content:

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.
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Title: An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination

Section 1: Introduction
Description 1: This section introduces the context and significance of fault detection in the power systems and the role of artificial neural networks (ANNs) in enhancing protection mechanisms.

Section 2: Faults in Power System
Description 2: This section describes the various types of faults that can occur in power systems, including symmetrical faults, unsymmetrical faults, and open circuit faults.

Section 3: Categories of Faults
Description 3: This section categorizes the different types of faults into symmetrical, unsymmetrical, and open circuit faults, providing a detailed discussion of each type.

Section 4: Artificial Neural Network
Description 4: This section discusses the fundamentals of artificial neural networks, their capabilities, learning algorithms, and their application in electrical power systems.

Section 5: Distance Protection by ANN
Description 5: This section explains the concept of distance protection in transmission lines using ANN and details various studies and methodologies that have applied ANN to improve distance protection schemes.

Section 6: Application of ANN on Transmission Line Protection
Description 6: This section presents the application of ANN in various aspects of transmission line protection, including fault detection, classification, location, direction discrimination, and faulty phase selection.

Section 7: Studies on "Fault Detection and Classification"
Description 7: This section reviews various studies that have applied ANN for fault detection and classification in transmission lines.

Section 8: Studies on "Fault Direction Discrimination"
Description 8: This section reviews the approaches and studies focusing on using ANN for fault direction discrimination in transmission lines.

Section 9: Studies on "Faulty Phase Selection"
Description 9: This section discusses different ANN-based schemes and studies for selecting the faulty phase during transmission line fault conditions.

Section 10: Conclusion
Description 10: This section summarizes the findings, conclusions, and potential implications of the studies reviewed in the paper regarding the application of ANN to transmission line protection.