Abstract
Power system fault detection has been an import area of study for power distribution networks. The power transmission systems often operate in the kV range with significant current flowing through the lines. A single fault, even lasting for a fraction of a second, can cause huge losses and manufacturing downtime for industrial applications. In this research, we develop an approach to detect, classify, and localize different types of phase-to-ground and phase-to-phase faults in three-phase power transmission systems based on discrete wavelet transform (DWT) and artificial neural networks (ANN). The multi-resolution property of wavelet transform provides a suitable tool to analyze the irregular transient changes in voltage or current signals in the network when fault occurs. An artificial neural network is employed to discriminate the types of fault based on features extracted by DWT. Computer simulation results show that this method can effectively identify various faults in a typical three-phase transmission line in power grid.
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References
Anderson, P.M.: Analysis of Faulted Power Systems. IEEE Press Series on Power Engineering. Wiley-IEEE Press, Piscataway (1995)
Glover, J.D., Sarma, M.S., Overbye, T.J.: Power System Analysis and Design, 5th edn. Cengage Learning, Boston (2012)
Kothari, D.P., Nagrath, I.J.: Modern Power System Analysis, 4th edn. McGraw-Hill Education, New York City (2011)
Magagula, X.G., Hamam, Y., Jordaan, J.A., Yusuff, A.A.: Fault detection and classification method using DWT and SVM in a power distribution network. In: 2017 IEEE PES Power Africa, Accra, pp. 1–6 (2017)
Fernandes, J.F., Costa, F.B., de Medeiros, R.P.: Power transformer disturbance classification based on the wavelet transform and artificial neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, pp. 640–646 (2016)
Ramaswamy, S., Kiran, B.V., Kashyap, K.H., Shenoy, U.J.: Classification of power system transients using wavelet transforms and probabilistic neural networks. In: Conference on Convergent Technologies for Asia-Pacific Region (IEEE TENCON 2003), vol. IV, pp. 1272–1276 (2003)
Charfi, F., Sellami, F., Al-Haddad, K.: Fault diagnostic in power system using wavelet transforms and neural networks. In: 2006 IEEE International Symposium on Industrial Electronics, Montreal, Quebec, pp. 1143–1148 (2006)
Haykin, S.: Neural Networks and Learning Machines. Prentice Hall/Pearson, New York (2009)
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Malla, P., Coburn, W., Keegan, K., Yu, XH. (2019). Power System Fault Detection and Classification Using Wavelet Transform and Artificial Neural Networks. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_27
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DOI: https://doi.org/10.1007/978-3-030-22808-8_27
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