This study presents an artificial neural-network (ANN)-based digital fault classification and location algorithm for medium voltage (MV) overhead power distribution lines with load taps and embedded remote-end source. The algorithm utilizes frequency spectra of voltage and current samples which are recorded by the digital relay at the substation. In the algorithm, to extract useful information for ANN inputs, the frequency spectral analysis of voltage and current waveforms has been carried out using Fast Fourier Transform. To classify and locate the shunt faults on an MV distribution system, a multilayer perceptron neural network (MLPNN) with the standard back-propagation technique has been used. A 34.5 kV overhead distribution system has been simulated using MATLAB/Simulink, and the results are used to train and test the ANNs. The technique takes into account all the practical aspects of real distribution system, such as errors, originated from instrument transformers and interface. The ANN-based fault location technique has been extensively tested for a realistic model and gives satisfactory results for radial overhead distribution systems with load taps and in the presence of remote-end source connection.

Artificial neural-network-based fault location for power distribution lines using the frequency spectra of fault data

Electric power transmission line is an important medium to supply the electric power continuously. However, the exposed environment of transmission line leads to the presence of faults. Therefore, this paper presented a method to overcome the problem in order to improve the quality of power system. The objective was to provide an accurate method for fault detection and to classify the fault that occurs in power transmission line using Artificial Neural Network (ANN). 14-bus system has been tested through MATLAB programming to create the fault using impedance technique. After carrying out the fault simulation, the result of faulty voltage and current was taken as an input to the neural network. The study implemented feed-forward ANN with backpropagation algorithm to develop the fault detection and classification. The performance of the detection and classification is measured using the Mean Square Error (MSE), linear regression and confusion matrix. The detection achieved a tolerable MSE of 5.5614e-8, a correlation of 1 and an accuracy of 100% proving the satisfactory performance of the system. While the classification achieved a tolerable MSE of 0.43699, a correlation of 0.83955 and an accuracy of 70% showing that the performance of the system is acceptable.

Fault Detection Method Using ANN for Power Transmission Line

Electric power Transmission lines are characterized by very lengthy transmission lines and thus are more exposed to the environment. Consequently, transmission lines are more prone to faults, which hinder the continuity of electric power supplied, increases the loss of electric power generated and loss of economy. Quick detection and classification of a fault hastens its Clearance and reduces system downtime thus, improving the security and efficiency of the network. Thus, this paper focuses on developing a single artificial neural network to detect and classify a fault on Nigeria 33-kV electric power transmission lines. This study employs feedforward artificial neural networks with backpropagation algorithm in developing the fault detector- classifier. The transmission lines were modeled using SimPowerSystems toolbox in Simulink and simulation is done in MATLAB environment. The instantaneous voltages and currents values are extracted and used to train the fault detector-classifier. Simulation results have been provided to demonstrate the efficiency of the developed intelligent systems for fault detection and classification on 33-kV Nigeria transmission lines. The performance of the detector-classifier is evaluated using the Mean Square Error (MSE) and the confusion matrix. The systems achieved an acceptable MSE of 0.00004279 and an accuracy of 95.7%, showing that the performance of the developed intelligent system is satisfactory. The result of the developed system in this work is better in comparison with other systems in the literature concerning Nigeria transmission lines.

An Artificial Neural Network-Based Intelligent Fault Classification System for the 33-kV Nigeria Transmission Line

This work presents the implementation of a trained Artificial Neural Network (ANN) for a certain application. A Multi Layer Perceptron (MLP) has been synthesized and implemented on Spartan3 FPGA. The weight matrices and bias has been provided by separate simulation software like MATLAB/Simulink. The implemented network has been verified in Xilinx ISE using Verilog programming language. The device utilization summary illustrates that the implemented perceptron utilizes few slices on FPGA which makes it suitable for large scale implementation. The implementation of FPGA based neural network is verified for a specific application. Keywords- Artificial Neural Network, FPGA implementation, Multilayer Perceptron(MLP), Verilog.

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FPGA Implementation of a Trained Neural Network

Transmission lines are generally susceptible to faults, and it is highly desirable and advantageous to locate and clear these faults at the fastest speed. Thus, the aim of this paper is to develop a fault locator using an artificial neural network that will detect, classify and locate a fault when it occurs on the 33-kV Nigeria transmission line network. The network is modeled and simulated in the MATLAB/Simulink environment. The training testing and evaluation of the intelligent locator is done based on a multilayer perceptron feedforward artificial neural network with backpropagation algorithm. The performance of the detectorclassifier and each locator was evaluated using Mean Square Error (MSE) and confusion matrix. The developed system is capable of detecting and classifying single line-to-ground faults, double lineto-ground faults, line-to-line faults, all the three lines short circuit faults and no fault condition; and locating line-to-ground, double line-to-ground faults and line-to-line faults. The detector-classifier achieved 91.5 % accuracy and 0.0022 MSE and the locators achieved 100% accuracy each and MSE of 0.000499, 0.000190157 and 0.000783 for single line-to-ground faults, double line-to-ground faults, and line-to-line faults, respectively. The result of the developed system in this work is better in comparison with other similar systems in the literature for locating faults on the Nigeria transmission line.

Artificial Neural Networks for Intelligent Fault Location on the 33-Kv Nigeria Transmission Line

Transmission and distribution lines are vital links between generating units and consumers. They are exposed to atmosphere, hence chances of occurrence of fault in transmission line is very high, which has to be immediately taken care of in order to minimize damage caused by it. This paper focuses on detecting the faults on electric power transmission lines using artificial neural networks. A feed forward neural network is employed, which is trained with back propagation algorithm. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. The developed neural network is capable of detecting single line to ground and double line to ground for all the three phases. Simulation is done using MATLAB Simulink to demonstrate that artificial neural network based method are efficient in detecting faults on transmission lines and achieve satisfactory performances. A 300km, 25kv transmission line is used to validate the proposed fault detection system. Hardware implementation of neural network is done on TMS320C6713. Keywords: Transmission Line, Asymmetric fault detection, Artificial neural network (ANN), Back Propagation algorithm, DSP processor TMS320C6713, Code Composer Studio, MATLAB/Simulink

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Thejaswini S

Papers

Intelligent Fault Identification System for Transmission Lines Using Artificial Neural Network