An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Fault Direction Discrimination". <s> 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. > <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 "Fault Direction Discrimination". <s> 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. <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 "Fault Direction Discrimination". <s> 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. <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 "Fault Direction Discrimination". <s> 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. <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 "Fault Direction Discrimination". <s> 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. <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 "Fault Direction Discrimination". <s> 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. <s> BIB006 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Fault Direction Discrimination". <s> 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. <s> BIB007 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Fault Direction Discrimination". <s> 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. <s> BIB008 </s> An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination <s> Studies on "Fault Direction Discrimination". <s> 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. <s> BIB009
Fault direction estimation on transmission line is very crucial for enhancing the performance of power system. Advancement of huge generating stations and highly interconnected power systems entails less fault clearing times. The approach of ANN has been positively utilized for the improvement of many of the standard functions that are operated in transmission lines. The accuracy of an electromechanical, static, or a microprocessor based distance relay is affected by different fault conditions and network configuration changes. Hence the direction of the fault should be discriminated to maintain the normal operation of the power system. Dalstein et al. have used ANN method to estimate the fault location process by means of directional discrimination. They have proposed a neural network to estimate the direction of the fault   . Authors  employed neural network for designing two different fault direction discrimination modules for high speed transmission line and found that fault direction can be identified quickly and accurately from their results. Table 3 highlights the different schemes BIB001 BIB002 BIB003 BIB006 BIB004 BIB005  BIB007 BIB008 BIB009 used for fault direction estimation with its response time and features of ANN along with remarks.