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Fault Location Estimation by Using Machine Learning Methods in Mixed Transmission Lines
European Journal of Science and Technology, 2020
Overhead lines are generally used for electrical energy transmission. Also, XLPE underground cable lines are generally used in the city center and the crowded areas to provide electrical safety, so high voltage underground cable lines are used together with overhead line in the transmission lines, and these lines are called as the mixed lines. The distance protection relays are used to determine the impedance based fault location according to the current and voltage magnitudes in the transmission lines. However, the fault location cannot be correctly detected in mixed transmission lines due to different characteristic impedance per unit length because the characteristic impedance of high voltage cable line is significantly different from overhead line. Thus, determinations of the fault section and location with the distance protection relays are difficult in the mixed transmission lines. In this study, 154 kV overhead transmission line and underground cable line are examined as the mixed transmission line for the distance protection relays. Phase to ground faults are created in the mixed transmission line, and overhead line section and underground cable section are simulated by using PSCAD/ EMTDC ™. The short circuit fault images are generated in the distance protection relay for the overhead transmission line and underground cable transmission line faults. The images include the R-X impedance diagram of the fault, and the R-X impedance diagram have been detected by applying image processing steps. The regression methods are used for prediction of the fault location, and the results of image processing are used as the input parameters for the training process of the regression methods. The results of regression methods are compared to select the most suitable method at the end of this study for forecasting of the fault location in transmission lines. When looking at the method and performance criteria used in the overhead transmission line fault location study, it is the Linear Regression (Robust Linear) method that gives the most accurate results with RMSE 0.017652. When looking at the method and performance criteria used in the underground cable transmission line fault location study, it is the Linear Regression (Stepwise Linear) method, which gives the most accurate results with RMSE 0.0060709. When the accuracy of the method was examined, it was seen that it was higher than other methods.
Power grid in India is laid almost with the overhead lines (OH). With the capacity of XLPE cables to transmit high voltages has led power system engineers to take keen interest in mixing OH lines with underground (UG) cables where the environmental effects, increasing population or the areas where right of way is a constraint for connecting the grid line. Faults in mixed system require broader aspects of consideration and analysis as the UG cable and OH lines both exhibits different characteristics. Conventional tracer and terminal methods are time consuming therefore computer based methods are emerging as a solution to remove their drawbacks and to provide more accurate results for fault detection, classification and location faster. This paper deals with use of ANN for protection of mixed line taking into account the parameters at each point for cables and sequence components for OH line. In MATLAB/SIMULINK software simulation is carried out for verification of results.
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.
arXiv: Signal Processing, 2020
It is very important to ensure continuity in the process from generation of electricity to transmission to cities. The most important part of the system is energy transmission lines and distance protection relays that protect these lines. The main function of the protection relays in electrical installations should be deactivated as soon as possible in the event of short circuits in the system. An accurate error location technique is required to make fast and efficient work. Distance relays are widely used as main and backup protection in transmission and distribution lines. Basically, distance protection relays determine the impedance of the line by comparing the voltage and current values. In this study, artificial neural network (ANN) has been used to accurately locate high impedance short circuit faults in 154 kV power transmission lines. The impedance diagram (R-X) of the circuit breaker, current-voltage transformer, overhead transmission line, distance protection relay and dis...
Nigerian Journal of Technology, 2015
This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic system (FDS) for the system (FDS) for the system (FDS) for the system (FDS) for the location of fault on transmission lines. The principal functions of this diagnostic system are location of fault on transmission lines. The principal functions of this diagnostic system are location of fault on transmission lines. The principal functions of this diagnostic system are location of fault on transmission lines. The principal functions of this diagnostic system are: : : : detection of fault occurrence, detection of fault occurrence, detection of fault occurrence, detection of fault occurrence, identification of faulted sections and classification of faults into types. This has been identification of faulted sections and classification of faults into types. This has been identification of faulted sections and classification of faults into types. This has been identification of faulted sections and classification of faults into types. This has been achieved through a cascaded, multilayer ANN structure using the back achieved through a cascaded, multilayer ANN structure using the back achieved through a cascaded, multilayer ANN structure using the back achieved through a cascaded, multilayer ANN structure using the back-propagation (BP) learning algorithm. This propagation (BP) learning algorithm. This propagation (BP) learning algorithm. This propagation (BP) learning algorithm. This paper shows that the FDS accurately identifies High Imp paper shows that the FDS accurately identifies High Imp paper shows that the FDS accurately identifies High Imp paper shows that the FDS accurately identifies High Impedance Faults, which are relatively difficult to identify with edance Faults, which are relatively difficult to identify with edance Faults, which are relatively difficult to identify with edance Faults, which are relatively difficult to identify with other methods. Test results are simulated and generated in MATLAB using Apo 132KV transmission line in Apo other methods. Test results are simulated and generated in MATLAB using Apo 132KV transmission line in Apo other methods. Test results are simulated and generated in MATLAB using Apo 132KV transmission line in Apo other methods. Test results are simulated and generated in MATLAB using Apo 132KV transmission line in Apo transmission substation, Abuja. These results amply demonstrate the capability of the transmission substation, Abuja. These results amply demonstrate the capability of the transmission substation, Abuja. These results amply demonstrate the capability of the transmission substation, Abuja. These results amply demonstrate the capability of the FDS in terms of accuracy and FDS in terms of accuracy and FDS in terms of accuracy and FDS in terms of accuracy and speed with respect to detection, localization, and classification of faults in transmission lines. speed with respect to detection, localization, and classification of faults in transmission lines. speed with respect to detection, localization, and classification of faults in transmission lines. speed with respect to detection, localization, and classification of faults in transmission lines.
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.
ANN Based Location of Fault for 500KV Transmission Line in Sudan from Merowe to Atbara
Journal of Karary University for Engineering and Science
Today, transmission lines have become one of the important core components in systems of electrical power that are used to transport energy. Since transmission lines are prone in nature, the probability of failures in transmission lines is generally higher than that of other major components. This paper presents the fault of the transmission line from Merowe to Atbara in northern Sudan using artificial neural networks. A transmission line model was created in MATLAB R2014a using SIMULINK and SIMSCAPE with the SIMPOWERSYSTEM toolbox. The current and voltage values obtained from the transmission line model have been used as an entry for artificial neural networks. The results obtained from the proposed artificial neural networks were acceptable and the networks were found to be practically practicable for implementation. The significance of picking the most proper artificial neural network is to get the best performance from the neural networks.
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.