A Novel Decision Tree Regression-Based Fault Distance Estimation Scheme for Transmission Lines (original) (raw)
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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.
A hybrid method for fault location estimation in a fixed series compensated lines
Measurement, 2018
This paper proposes a fault distance estimation scheme for fixed series capacitor compensated parallel transmission lines using discrete wavelet transform and decision tree regression. The purpose of the data mining based scheme is to avoid the complicated equation based methods that have been suggested by researchers to overcome the drawbacks of conventional fault location scheme. Although decision tree has inherent advantage over other methods like artificial neural network and support vector machines to work with large data sets, it has not been used in fault location estimation in series compensated (SC) transmission line so far. Decision tree is chosen to locate the faults because of its ability to work with large data set and high accuracy in associating the fault pattern to the fault distance using regression analysis. The discrete wavelet transform processed signals makes the decision process of decision tree regression easy by providing appropriate features. The proposed method is evaluated with variation of fault location, fault type, pre-fault load angle, location of series capacitor, degree of series compensation, fault inception angle, line parameters, inter-circuit faults and fault resistance. The test result of decision tree regression based location estimation scheme ensures that, it can estimate the fault distance accurately.
High Voltage, 2016
A comprehensive review on the methods used for fault detection, classification and location in transmission lines and distribution systems is presented in this study. Though the three topics are highly correlated, the authors try to discuss them separately, so that one may have a more logical and comprehensive understanding of the concepts without getting confused. Great significance is also attached to the feature extraction process, without which the majority of the methods may not be implemented properly. Fault detection techniques are discussed on the basis of feature extraction. After the overall concepts and general ideas are presented, representative works as well as new progress in the techniques are covered and discussed in detail. One may find the content of this study helpful as a detailed literature review or a practical technical guidance.
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.
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.
Accurate fault location algorithm for power transmission lines
European Transactions on Electrical Power, 2007
In this paper we propose a new fault location algorithm for power transmission lines based on one terminal voltage and current data. A distributed time domain model of the line is used as a basis for algorithm development. The suggested technique only takes advantage ofpost-fault voltage and current samples taken at one end of the line and does not require filtering of DC offset and high-frequency components of the recorded signals, which are present during transient conditions. Another advantage of the proposed method is the application of a very narrow window of data i. e. less than 1/4 of a cycle. The paper also proposes two different algorithms for lossless and lossv line models. Computer simulations approved the accuracy of the proposed methods.
Measurement, 2019
In this paper, decision tree aided travelling wave (TW) based fault section identification and location scheme has been proposed for multi-terminal transmission lines (MTTLs). The difference between fault inception time and travelling wave arrival time recorded is termed as travel time (TT) which is used to identify the faulty section. The decision is based on simple rule comparing the measured TT with the actual TT possible for the wave to travel along an individual transmission line. Upon recognizing the faulty section, the two adjacent arrival times of the faulty section can be used to estimate the fault location. The proposed scheme can also solve different issues that are prevailing in different transmission networks such as two-terminal transmission lines, series compensated transmission lines (SCTLs) and MTTLs. The performance of the proposed scheme is verified for different challenging fault scenarios by varying different fault parameters.
FAULT LOCATION AND DISTANCE ESTIMATION ON POWER TRANSMISSION LINES USING DISCRETE WAVELET TRANSFORM
1963
Fault location is very important in power system engineering in order to clear fault quickly and restore power supply as soon as possible with minimum interruption. In this study a 300km, 330kv, 50Hz power transmission line model was developed and simulated using power system block set of MATLAB to obtain fault current waveforms. The waveforms were analysed using the Discrete Wavelet Transform (DWT) toolbox by selecting suitable wavelet family to obtain the pre-fault and post-fault coefficients for estimating the fault distance. This was achieved by adding non negative values of the coefficients after subtracting the pre-fault coefficients from the post-fault coefficients. It was found that better results of the distance estimation, were achieved using Daubechies ‘db5’wavele,t with an error of three percent (3%).
International Conference on Electrical Engineering (ICEENG'6), Military Technical College, 2008
This paper presents a new fault detector and locator scheme based on (DWT) and (ANN) for transmission lines. The main idea is to estimate faults detection, faulted phases distinguishing and faults location. These processes are obtained by calculating standard deviation of output signals from discrete wavelet analysis for all phase's currents signals. The final results will be obtained by training the proposed ANNs. The scheme has been implemented under Matlab-7-with utilization of toolboxes such as Simulink, WT and ANN. A typical 220 kv transmission system with 100 km of transmission lines has been simulated to evaluate the studied scheme. The results show that the proposed scheme is efficient and easy in implement. Also, it is capable to detect, classify and locate varies faults within a half cycle after their occurrences.
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.