Fault Identification, Classification, and Location on Transmission Lines Using Combined Machine Learning Methods
This study develops a hybrid method to identify, classify, and locate electrical faults on transmission lines based on Machine Learning (ML) methods. Firstly, Wavelet Transform (WT) technique is applied to extract features from the current or voltage signals. The extracted signals are decomposed into eleven coefficients. These coefficients are calculated to the energy level, and the data of teen fault types are converted to the RGB image. Secondly, GoogLeNet model is applied to classify the fault, and Convolutional Neural Network (CNN) method is proposed to locate the fault. The proposed method is tested on the four-bus power system with the 220 kV transmission line via time-domain simulation using Matlab software. The conditions of the fault resistance random values and the pre-fault load changes are considered. The simulation results show that the proposed method has high accuracy and fast processing time, and is a useful tool for analyzing the system stability in the field of electricity.
L. V. Dai, D. D. Tung, and L. C. Quyen, “A Highly Relevant Method for Incorporation of Shunt Connected FACTS Device into Multi-Machine Power System to Dampen Electromechanical Oscillations,” Energies, vol. 10, no. 4, 482, April 2017.
L. V. Dai, D. D. Tung, T. L. T. Dong, and C. L. Quyen, “Improving Power System Stability with Gramian Matrix-Based Optimal Setting of a Single Series Facts Device: Feasibility Study in Vietnamese Power System,” Complexity, vol. 2017, 3014510, January 2017.
Ravi, “Causes, Nature and Effect of Fault in Power in Power System,” http://electricalarticle.com/causes-nature-effect-fault-power-system, December 26, 2019.
C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, September 1995.
U. B. Parikh, B. Das, and R. Maheshwari, “Fault Classification Technique for Series Compensated Transmission Line Using Support Vector Machine,” International Journal of Electrical Power Energy Systems, vol. 32, no. 6, pp. 629-636, July 2010.
P. Ray and D. P. Mishra, “Support Vector Machine Based Fault Classification and Location of a Long Transmission Line,” Engineering Science Technology, an International Journal, vol. 19, no. 3, pp. 1368-1380, September 2016.
N. R. Babu and B. J. Mohan, “Fault Classification in Power Systems Using EMD and SVM,” Ain Shams Engineering Journal, vol. 8, no. 2, pp. 103-111, June 2017.
Y. Guo, K. Li, and X. Liu, “Fault Diagnosis for Power System Transmission Line Based on PCA and SVMs,” International Conference on Intelligent Computing for Sustainable Energy and Environment, pp. 524-532, September 2012.
V. Malathi and N. Marimuthu, “Multi-Class Support Vector Machine Approach for Fault Classification in Power Transmission Line,” IEEE International Conference on Sustainable Energy Technologies, pp. 67-71, November 2008.
H. Livani and C. Y. Evrenosoğlu, “A Fault Classification Method in Power Systems Using DWT and SVM Classifier,” IEEE PES Transmission and Distribution Conference and Exposition, pp. 1-5, May 2012.
V. Ferreira, R. Zanghi, M. Fortes, G. Sotelo, R. Silva, J. Souza, et al., “A Survey on Intelligent System Application to Fault Diagnosis in Electric Power System Transmission Lines,” Electric Power Systems Research, vol. 136, pp. 135-153, July 2016.
A. Prasad, J. B. Edward, and K. Ravi, “A Review on Fault Classification Methodologies in Power Transmission Systems: Part-I,” Journal of Electrical Systems, vol. 5, no. 1, pp. 48-60, May 2018.
H. K. Zadeh and M. Aghaebrahimi, “A Novel Approach to Fault Classification and Fault Location for Medium Voltage Cables Based on Artificial Neural Network,” International Journal of Computational Intelligence, vol. 2, no. 1, pp. 1304-2386, 2005.
S. Ekici, S. Yildirim, and M. Poyraz, “Energy and Entropy-Based Feature Extraction for Locating Fault on Transmission Lines by Using Neural Network and Wavelet Packet Decomposition,” Expert Systems with Applications, vol. 34, no. 4, pp. 2937-2944, May 2008.
Y. S. Rao, G. R. Kumar, and G. K. Rao, “A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks,” International Journal of Power Electronics, vol. 8, no. 1, 505, 2017.
M. Saradarzadeh and M. S. Pasand, “An Accurate Fuzzy Logic‐Based Fault Classification Algorithm Using Voltage and Current Phase Sequence Components,” International Transactions on Electrical Energy Systems, vol. 25, no. 10, pp. 2275-2288, October 2015.
A. Prasad, J. B. Edward, C. S. Roy, G. Divyansh, and A. Kumar, “Classification of Faults in Power Transmission Lines Using Fuzzy-Logic Technique,” Indian Journal of Science, vol. 8, no. 30, pp. 1-6, 2015.
S. Adhikari, N. Sinha, and T. Dorendrajit, “Fuzzy Logic Based On-Line Fault Detection and Classification in Transmission Line,” SpringerPlus, vol. 5, no. 1, pp. 1-14, July 2016.
D. Thukaram, H. Khincha, and H. Vijaynarasimha, “Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems,” IEEE Transactions on Power Delivery, vol. 20, no. 2, pp. 710-721, April 2005.
P. Nonyane, “The Application of Artificial Neural Networks to Transmission Line Fault Detection and Diagnosis,” Ph.D. dissertation, University of South Africa, Pretoria, 2016.
M. Farshad and J. Sadeh, “Accurate Single-Phase Fault-Location Method for Transmission Lines Based on K-Nearest Neighbor Algorithm Using One-End Voltage,” IEEE Transactions on Power Delivery, vol. 27, no. 4, pp. 2360-2367, September 2012.
P. Ray, “Fast and Accurate Fault Location by Extreme Learning Machine in a Series Compensated Transmission Line,” Power and Energy Systems: Towards Sustainable Energy, pp. 1-6, March 2014.
K. Hosseini, “Short Circuit Fault Classification and Location in Transmission Lines Using a Combination of Wavelet Transform and Support Vector Machines,” International Journal on Electrical Engineering Informatics, vol. 7, no. 2, 353, June 2015.
E. U. Haq, H. Jianjun, K. Li, F. Ahmad, D. Banjerdpongchai, and T. Zhang, “Improved Performance of Detection and Classification of 3-Phase Transmission Line Faults Based on Discrete Wavelet Transform and Double-Channel Extreme Learning Machine,” Electrical Engineering, vol. 103, no. 2, pp. 953-963, April 2021.
P. Ray and D. Mishra, “Application of Extreme Learning Machine for Underground Cable Fault Location,” International Transactions on Electrical Energy Systems, vol. 25, no. 12, pp. 3227-3247, December 2015.
P. Ray, D. P. Mishra, K. Dey, and P. Mishra, “Fault Detection and Classification of a Transmission Line Using Discrete Wavelet Transform & Artificial Neural Network,” International Conference on Information Technology, pp. 178-183, December 2017.
P. Ray, D. P. Mishra, and S. Mohaptra, “Fault Classification of a Transmission Line Using Wavelet Transform & Fuzzy Logic,” 1st International Conference on Power Electronics, Intelligent Control, and Energy Systems, pp. 1-6, July 2016.
C. K. Jung, K. H. Kim, J. B. Lee, and B. Klöckl, “Wavelet and Neuro-Fuzzy Based Fault Location for Combined Transmission Systems,” International Journal of Electrical Power and Energy Systems, vol. 29, no. 6, pp. 445-454, July 2007.
M. Shafiullah, M. A. Abido, and Z. A. Hamouz, “Wavelet-Based Extreme Learning Machine for Distribution Grid Fault Location,” IET Generation, Transmission, and Distribution, vol. 11, no. 17, pp. 4256-4263, November 2017.
S. Thukral, O. P. Mahela, and B. Kumar, “Detection of Transmission Line Faults in the Presence of Wind Energy Power Generation Source Using Stockwell’s Transform,” International Conference on Issues and Challenges in Intelligent Computing Techniques, pp. 1-6, September 2019.
D. P. Mishra and P. Ray, “Fault Detection, Location and Classification of a Transmission Line,” Neural Computing and Applications, vol. 30, no. 5, pp. 1377-1424, September 2018.
P. Malla, W. Coburn, K. Keegan, and X. H. Yu, “Power System Fault Detection and Classification Using Wavelet Transform and Artificial Neural Networks,” International Symp. on Neural Networks, pp. 266-272, July 2019.
G. Cao, K. Zhang, K. Zhou, H. Pan, Y. Xu, and J. Liu, “A Feature Transferring Fault Diagnosis Based on WPDR, FSWT and GoogLeNet,” IEEE International Instrumentation and Measurement Technology Conference, pp. 1-6, May 2020.
M. F. Guo, N. C. Yang, and W. F. Chen, “Deep-Learning-Based Fault Classification Using Hilbert-Huang Transform and Convolutional Neural Network in Power Distribution Systems,” IEEE Sensors Journal, vol. 19, no. 16, pp. 6905-6913, April 2019.
T. M. Lai, L. A. Snider, E. Lo, and D. Sutanto, “High-Impedance Fault Detection Using Discrete Wavelet Transform and Frequency Range and RMS Conversion,” IEEE Transactions on Power Delivery, vol. 20, no. 1, pp. 397-407, February 2005.
S. G. 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.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., “Going Deeper with Convolutions,” Proc. of the IEEE Conference on Computer Vsion and Pattern Recognition, pp. 1-9, October 2015.
J. W. Liu, F. L. Zuo, Y. X. Guo, T. Y. Li, and J. M. Chen, “Research on Improved Wavelet Convolutional Wavelet Neural Networks,” Applied Intelligence, vol. 50, no. 11, pp. 1-21, November 2020.
Copyright (c) 2022 Nguyen Nhan Bon, Van Dai Le
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.
Since Jan. 01, 2019, IJETI will publish new articles with Creative Commons Attribution Non-Commercial License, under Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.