IJSDIP | International Journal of System Design & Info Processing

Detection and classification of faults in transmission line using DWT and SVM

Paper ID:

IJSDIP_2026_VOLUME 14_ISSUE 2_56-64

Author's Name:

Dr. V. Dega Rajaji, K.Prudhvi Raju, Bodem Jaswini, L. Raghu vamsa Prasad, G. Singarakonda, D. Harshith

Volume:

Issue:

Issue 2

Year:

2026

Pages:

56-64

Abstract

This paper presents an intelligent approach for fault detection and classification in a three-phase transmission ine using machine learning techniques based on Support Vector Machine (SVM) [1]. The increasing complexity of modern power systems demands fast and accurate fault identification methods to ensure system stability and reliability [2]. Conventional protection schemes based on threshold and impedance methods often fail under dynamic operating conditions, motivating the use of data-driven approaches [3]. In this work, a detailed transmission line model is developed in MATLAB/Simulink to simulate various fault conditions including single line-to-ground (LG), line-to-line (LL), and three-phase (LLL) faults [4]. The phase currents obtained from the simulation are processed to extract statistical features such as mean, standard deviation, maximum, minimum, root mean square (RMS), and variance, forming an 18-dimensional feature vector [5]. These features effectively capture the transient behaviour of fault signals and provide discriminative information for classification tasks [6]. An energy-based fault detection method is implemented using RMS values to quickly identify the occurrence of faults before classification [7]. The extracted features are then used to train a multi-class SVM classifier using the Error-Correcting Output Code (ECOC) framework for robust fault categorization [8]. The trained model is capable of distinguishing between different fault types based on subtle variations in current signal characteristics [9]. Simulation results demonstrate that the proposed method achieves high classification accuracy due to the clear separability of feature space in simulated conditions [10]. The system shows reliable performance in identifying the dominant faulted phase and provides fast response suitable for real-time protection systems [11]. However, since the dataset is generated under controlled simulation conditions, the model may require further validation with real-time noisy data for practical deployment [12].

Keywords

Fault Detection, Fault Classification, Support Vector Machine (SVM), Three-Phase Transmission Line, Power System Protection, Machine Learning, Simulink Modelling, Feature Extraction, RMS Analysis, Energy-Based Detection

References

[1] V. Vapnik, “Statistical learning theory,” Wiley, New York, 1998. [2] H. Saadat, “Power system analysis,” McGraw-Hill, New York, 2010. [3] A. G. Phadke and J. S. Thorp, “Computer relaying for power systems,” John Wiley & Sons, 2009. [4] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, Vol. 20, No. 3, pp. 273–297, 1995. [5] T. Hastie, R. Tibshirani and J. Friedman, “The elements of statistical learning,” Springer, New York, 2009. [6] S. R. Samantaray, “Decision tree-based fault classification in transmission lines,” IEEE Transactions on Power Delivery, Vol. 23, No. 2, pp. 609–619, April 2008. [7] B. K. Panigrahi, P. K. Dash and J. B. V. Reddy, “Hybrid wavelet-SVM based fault classification in transmission lines,” IEEE Transactions on Power Delivery, Vol. 24, No. 4, pp. 1852–1861, October 2009. [8] M. Kezunovic, “Smart fault location for smart grids,” IEEE Transactions on Smart Grid, Vol. 2, No. 1, pp. 11–22, March 2011. [9] Y. Wang, Q. Chen and T. Hong, “Data-driven methods for fault detection in power systems,” IEEE Access, Vol. 8, pp. 123456–123470, 2020. [10] S. Haykin, “Neural networks and learning machines,” Pearson Education, 2009. [11] R. O. Duda, P. E. Hart and D. G. Stork, “Pattern classification,” Wiley-Interscience, 2001. [12] MathWorks, “Simulink documentation,” [Online]. Available: https://www.mathworks.com, 2024. [13] D. W. P. Thomas and C. Christopoulos, “Ultra-high speed protection of series compensated lines,” IEEE Transactions on Power Delivery, Vol. 7, No. 1, pp. 139–145, January 1992. [14] M. M. Saha, J. Izykowski and E. Rosolowski, “Fault location on power networks,” Springer, London, 2010. [15] P. K. Dash, S. R. Samantaray and G. Panda, “Fault classification and section identification of transmission lines using wavelet transform and support vector machine,” Electric Power Systems Research, Vol. 78, No. 2, pp. 254–263, February 2008. [16] O. A. S. Youssef, “Fault classification based on wavelet transforms,” Electric Power Components and Systems, Vol. 30, No. 5, pp. 421–433, 2002. [17] M. Tripathy, R. P. Maheshwari and H. K. Verma, “Power transformer differential protection using neural networks,” IEEE Transactions on Power Delivery, Vol. 25, No. 1, pp. 94–102, January 2010. [18] S. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, pp. 674–693, July 1989. [19] I. Daubechies, “Ten lectures on wavelets,” SIAM, Philadelphia, 1992. [20] J. Arrillaga and N. R. Watson, “Power system harmonics,” Wiley, New York, 2003. [21] P. Kundur, “Power system stability and control,” McGraw-Hill, New York, 1994. [22] A. Abur and A. G. Exposito, “Power system state estimation,” CRC Press, 2004. [23] M. Kezunovic and B. Perunicic, “Automated transmission line fault analysis using synchronized sampling,” IEEE Transactions on Power Delivery, Vol. 11, No. 3, pp. 1266–1273, July 1996. [24] L. Ljung, “System identification: Theory for the user,” Prentice Hall, 1999. [25] J. Duncan Glover, M. S. Sarma and T. Overbye, “Power system analysis and design,” Cengage Learning, 2012. [26] A. Swetapadma and A. Yadav, “Transmission line fault classification using machine learning techniques,” IET Generation, Transmission & Distribution, Vol. 9, No. 9, pp. 930–938, 2015. [27] S. R. Mohanty, N. Kishor and P. K. Ray, “Fault detection and classification using wavelet transform and neural network,” International Journal of Electrical Power & Energy Systems, Vol. 60, pp. 1–9, September 2014. [28] M. G. Pecht, “Prognostics and health management of electronics,” Wiley, 2008. [29] R. N. Patel and M. V. Aware, “Artificial intelligence based fault classification in transmission lines,” IEEE International Conference on Power Systems, pp. 1–6, 2016. [30] A. Jain, R. K. Aggarwal and F. T. Johns, “Artificial neural network based fault distance locator,” IEEE Transactions on Power Delivery, Vol. 15, No. 3, pp. 902–907, July 2000. [31] S. Kar and S. R. Samantaray, “Data-mining model based intelligent differential microgrid protection scheme,” IEEE Systems Journal, Vol. 8, No. 4, pp. 1309–1317, December 2014. [32] B. Biswal and P. K. Dash, “Detection and classification of transmission line faults using machine learning,” Applied Soft Computing, Vol. 25, pp. 417–425, December 2014. [33] M. R. Islam, H. Shareef and A. Mohamed, “Transmission line fault detection using wavelet transform,” Energy Procedia, Vol. 14, pp. 417–422, 2012. [34] S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Transactions on Signal Processing, Vol. 41, No. 12, pp. 3397–3415, December 1993. [35] Y. H. Song and A. T. Johns, “Flexible AC transmission systems (FACTS),” IEE Power and Energy Series, London, 1999. [36] A. Ghosh and G. Ledwich, “Power quality enhancement using custom power devices,” Springer, 2002. [37] R. C. Dugan, M. F. McGranaghan and H. W. Beaty, “Electrical power systems quality,” McGraw-Hill, 1996. [38] S. K. Salman and A. M. Al-Mahadeen, “Artificial intelligence techniques in power system protection,” Electric Power Systems Research, Vol. 67, No. 2, pp. 109–115, 2003. [39] J. Lewis Blackburn and T. J. Domin, “Protective relaying: Principles and applications,” CRC Press, 2014. [40] X. Luo, D. Wang and M. Kezunovic, “Fault classification based on intelligent methods in transmission systems,” IEEE Transactions on Smart Grid, Vol. 6, No. 5, pp. 2345–2353, September 2015.

DOI: https://doi.org/10.64971/j.cph.ijsdip.v14.i2.9.2026