Journal Paper

Paper Title - Fault Location: Classification and Detection of Extra High Voltage Transmission Line in Myanmar by Using Artificial Neutral Network Application


Abstract
This paper presents an implemented methodology for Fault Classifier and Fault Location occurring on a double circuit Transmission lines in Myanmar using Artificial Neural Network (ANN). The proposed algorithm uses the voltage and current signals of each section measured to detect and classify of faults. ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. The adaptive protection scheme based on application of ANN is tested for various faults, fault resistance and fault inception angle. An improved performance is experienced once the neural network is trained adequately, gives the accurate results when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within one cycle; thus the proposed adaptive protection technique is well suited for teed transmission circuit fault detection and classification. The proposed neural network-based module can improve the performance of conventional fault section algorithms. Keywords–Transmission line, Fault detector, Fault locator, Fault classifier, Artificial Neural Networks.


Author - Koko Aung, Soe Soe Ei Aung, Zeya Oo

Citation - Koko Aung   ,   Soe Soe Ei Aung   ,   Zeya Oo   ,   Koko Aung, Soe Soe Ei Aung, Zeya Oo " Fault Location: Classification and Detection of Extra High Voltage Transmission Line in Myanmar by Using Artificial Neutral Network Application " , International Journal of Industrial Electronics and Electrical Engineering , Volume-5,Issue-5  ( May, 2017 )

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| Published on 2017-07-14