Estimation of High Impedance Fault Location in Electrical Transmission Lines Using Artificial Neural Networks and R-X Impedance Graph (original) (raw)
Related papers
Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines
Advances in Artificial Intelligence, 2013
This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits). Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.
Fault classification and location of power transmission lines using artificial neural network
2007
This paper describes the application of an artificial neural network (ANN) based algorithm with modular structure to the fault classification and location of a single-circuit high voltage transmission line. Different fault types containing single-phase to ground, two-phase, two-phase to ground and three-phase are considered. The variation of fault resistance is considered, too. The operation of proposed strategy is not dependent on fault inception angle (FIA). A new classification method is proposed for decreasing of training time and dimensions of ANN. Using the proposed method, high accuracy of fault classification is achieved. Fundamental component of pre-fault and post-fault positive sequence component of currents and voltages of three phases have been used as inputs to proposed ANN. The output of the ANN is the estimated fault location. A two machine power system model is simulated by PSCAD/EMTDC to obtain the mentioned voltages and currents values. The neural network toolbox of MATLAB is used for training and testing of ANN.
An Alternative Approach for Location of Transmission Line Faults based on Artificial Neural Network
This paper describes the application of an artificial neural network (ANN) for location of transmission line faults. Three phase post fault samples of currents and voltages taken at one end of transmission line are used as inputs. Simulation studies have been carried out extensively on two power system models : one in which transmission line is fed from one end and the other in which transmission line is fed from both ends.The models are subjected to different types of faults at different operating conditions for variations in fault location, fault inception angle and fault point resistance. The results presented confirm the feasibility of the proposed scheme.
Artificial Neural Network Based Fault Classification and Location for Transmission Lines
2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE), 2019
Due to various faults occur to transmission lines and because it was necessary to find and recover these faults quickly as possible. This paper discussing fault detection, classification and determining fault location as fast as possible via Artificial Neural Network (ANN) algorithm. The software used for modeling the proposed network is a MATLAB/SIMULINK software environment. The training, testing and evaluation of the intelligent locator processes are done based on a multilayer Perceptron feed forward neural network with back propagation algorithm. Mean Square Error (MSE) algorithm is used to evaluate the performance of the detector/classifier as well as fault locator. The results show that the validation performance (MSE) for the fault detector/classifier is 2.36e-9 and for fault locator is 2.179e-5. The system can detect if there is a fault or not, can classify the fault type and determine the fault location very precisely.
Artificial Neural Network based Fault Classifier and Locator for Transmission Line Protection
This paper presents a method for classification and location of transmission line faults based on Artificial Neural Network (ANN). Although for fault classification prefault and postfault three phase currents samples taken at one end of transmission line are used as ANN inputs, for location of faults post fault samples of both current and voltages of three phases are required. Simulation studies have been carried out extensively on two power system models: one in which the transmission line is fed from one end and another, in which the transmission line is fed from two ends. Different types of faults at different operating conditions have been considered for carrying out simulation studies. The simulation results presented confirm the feasibility of the proposed approach.
Artificial neural network approach for locating faults in power transmission system
Eurocon 2013, 2013
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.
Transmission lines, among the other electrical power system components, suffer from unexpected failures due to various random causes. These failures interrupt the reliability of the operation of the power system. When unpredicted faults occur protective systems are required to prevent the propagation of these faults and safeguard the system against the abnormal operation resulting from them. The functions of these protective systems are to detect and classify faults as well as to determine the location of the faulty line as in the voltage and/or current line magnitudes Then after the protective relay sends a trip signal to a circuit breaker(s) in order to disconnect (isolate) the faulty line.
Artificial Neural Network-Based Fault Distance Locator for
2013
This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits). Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.
Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.
Transmission line fault distance and direction estimation using artificial neural network
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 upto 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.