Enhanced Real Time and Off-Line Transmission Line Fault Diagnosis Using Artificial Intelligence
Keywords:
Transmission Line Fault, Fault Location, Ground Faults, Power Transmission Line SystemAbstract
This paper is on “Enhanced Real Time and Off-Line Transmission Line Fault Diagnosis Using Artificial Intelligence”. The actual implementation and development of the neural networks and their architectures have been achieved for the three different parts of the fault diagnosis process namely fault detection, classification and fault location. This paper also gives an overview of the training and testing processes employed with neural networks. Series of simulation results that have been obtained using MATLAB, Sim Power Systems and the Artificial Neural Networks Toolboxes in Simulink are also presented in detail to emphasize the efficiency and accuracy factors of the proposed fault locator. Several neural networks with varying configurations have been trained, tested and their performances have been analyzed in this work.Downloads
References
Djuric, M. B., Radojevic, Z., M., Terzija, V. V. (2016). “Distance Protection and Fault Location Utilizing Only Phase Current Phasors.” IEEE Transactions of Power Delivery, vol. 13(4), pp. 1020-1026.
Edmund, O. (2016). “A Review of Impedance-Based Fault Locating expErience.” Proceedings of the 15th Annual Western Protective Relay Conference, Spokane, WA, pp. 24-27.
El-Sharkawi, M., & Niebur, D. (1996). “A Tutorial Course on Artificial Neural Networks with Applications to Power Systems.” IEEE, vol. 96TP, pp. 112-130.
Eriksson, L. & Rockefeller, G., D. (2015). “An Accurate Fault Locator with Compensation for Apparent Reactance in the Fault Resistance Resulting from Remote-End Feed.” IEEE Trans on PAS, vol. 104(2), pp. 424-436.
Girgis, A. A., Hart, D. G., & Peterson, W. L. (1992). “A New Fault Location Techniques for Two and Three Terminal Lines.” IEEE Transactions on Power Delivery vol. 7(1), pp. 98-107.
Haykin, S. (1994). “Neural Networks: A Comprehensive Foundation. Macmillan Collage Publishing Company, Inc. New York.
Howard, D., Mark, B., & Martin, H. (2012). The MathWorks User’s Guide for MATLAB and Simulink. Neural Networks Toolbox 6.
IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines. (2015). IEEE Power Engineering Society Publication. New York, IEEE Std, vol. C37. pp. 114.
Karl Z., & David C. (2015). Impedance-Based Fault Location Experience. Schweitzer Engineering Laboratories, Inc. Pullman, WA USA.
Kasztenny, B., Sharples, D., & Asaro, V. (2011). “Distance Relays and capacitive voltage transformers – balancing speed and transient overreach.” Proceedings of 55th Annual Georgia Tech Protective Relaying Conference, vol. 2, pp. 6-15.
Kezunovic, M. (2011). “A survey of Neural Net Applications to Protective Relaying and Fault Analysis.” International Journal of Engineering Intelligent Systems for Electronics, Engineering and Communications, vol. 5(4), pp. 185-192.
Kezunovic, M., Rikalo, I., & Sobajic, D. J. (1996). “Real-Time and Off-Line Transmission Line Faulty Classification Using Neural Networks.” Engineering Intelligent Systems, vol. 10, pp. 57-63.
Lahiri, U., Pradhan, A. K., & Mukhopadhyaya, S. (2015). “Modular Neural-Network Based Directional Relay for Transmission Line Protection.” IEEE Trans. on Power Delivery, vol. 20(4), pp. 2154-2155.
Yuan, L., & Ning, G. (2009). “Fault Location Algorithms Without Utilizing Line Parameters Based on Distributed Parameter Line Model.” IEEE Transactions on Power Delivery, vol. 24(2 ), pp. 579-584.
Zhang, Y., Zhang, Q., & Song, W. (2013). “Transmission Line Fault Location for Double Phase to- Earth Fault on Non-Direct-Ground Neutral System.” IEEE Transactions on Power Delivery, vol. 15(2), pp. 520-524.
Ziegler, G. (2006). “Numerical Distance Protection, Principles and Applications.” Siemens AG, Publicis MCD Verlag, Erlangen. Pp 22-43.
Downloads
Published
How to Cite
Issue
Section
License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
