Fault Classification and Detection in Transmission Lines by Chimp optimization algorithm (ChOA) Associated support Vector Machine
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Published:
Mar 31, 2025
Keywords:
Support Vector Machine, ChoA, Transmission Lines, Optimization
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Abstract
This paper presents a new method for fault classification and detection in transmission lines using the Chimp Optimization Algorithm (ChOA) integrated with Support Vector Machine (SVM). The adding complexity of modern power schemes demands more accurate and efficient methods for identifying and classifying faults to ensure system reliability and minimize downtime. The proposed method leverages the strengths of ChOA, an optimization procedure motivated by the social actions of chimpanzees, to optimize the parameters of the SVM, enhancing its ability to classify different types of faults accurately. By combining ChOA with SVM, the method not only improves fault classification accuracy but also reduces computational time, making it suitable for real-time applications. Extensive simulations were conducted using various fault scenarios and configurations to justify the execution of the ChOA-SVM model. The outcomes demonstrate that the suggested approach outperforms traditional fault detection procedures in precision, speed, and adaptability to different transmission line conditions. This study aids to the field of power system protection by giving a robust and efficient solution for fault classification and detection, with potential applications in enhancing the resilience and reliability of transmission networks..
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Kumar, V. R., P.Aruna Jeyanthy, & Mahesh K. (2025). Fault Classification and Detection in Transmission Lines by Chimp optimization algorithm (ChOA) Associated support Vector Machine. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 6(1), 86 - 101. https://doi.org/10.62527/jitsi.6.1.284
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References
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DOI: http://doi.org/10.11591/ijeecs.v20.i1.pp231-238
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13. Najafzadeh, M., Pouladi, J., Daghigh, A., Beiza, J., & Abedinzade, T. (2024). Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning Algorithms. International Journal of Computational Intelligence Systems, 17(1), 49. DOIhttps://doi.org/10.1007/s44196-024-00434-7
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15. Gomathy, V., & Selvaperumal, S. (2016). Fault detection and classification with optimization techniques for a three-phase single-inverter circuit. Journal of Power Electronics, 16(3), 1097-1109. DOI:10.6113/JPE.2016.16.3.1097
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17. Jawad, R. S., & Abid, H. (2022). Fault detection in HVDC system with gray wolf optimization algorithm based on artificial neural network. Energies, 15(20), 7775. https://doi.org/10.3390/en15207775
18. Jawad, R. S., & Abid, H. (2023). Fault detection and classification for voltage source converter-high voltage systems by using different swarm optimization algorithms-based neural network. Engineered Science, 23(2), 884.DOI:10.30919/es884
19. Ghashghaei, S., & Akhbari, M. (2021). Fault detection and classification of an HVDC transmission line using a heterogenous multi‐machine learning algorithm. IET Generation, Transmission & Distribution, 15(16), 2319-2332.https://doi.org/10.1049/gtd2.12180
20. Kanwal, S., & Jiriwibhakorn, S. (2023). Artificial intelligence based faults identification, classification, and localization techniques in transmission lines-a review. IEEE Latin America Transactions, 21(12), 1291-1305. DOI:10.1109/TLA.2023.10305233
21. Ray, P., & Mishra, D. P. (2016). Support vector machine based fault classification and location of a long transmission line. Engineering science and technology, an international journal, 19(3), 1368-1380. https://doi.org/10.1016/j.jestch.2016.04.001
22. Kumar, A. N., Kumar, M. S., Ramesha, M., Gururaj, B., & Srikanth, A. (2021). Support vector machine based fault section identification and fault classification scheme in six phase transmission line. IAES International Journal of Artificial Intelligence, 10(4), 1019. DOI: http://doi.org/10.11591/ijai.v10.i4.pp1019-1024
23. Fei, C., & Qin, J. (2021). Fault location after fault classification in transmission line using voltage amplitudes and support vector machine. Russian Electrical Engineering, 92(2), 112-121. DOI:10.1016/j.epsr.2018.02.005
24. Shakiba, F. M., Azizi, S. M., Zhou, M., & Abusorrah, A. (2023). Application of machine learning methods in fault detection and classification of power transmission lines: a survey. Artificial Intelligence Review, 56(7), 5799-5836. https://doi.org/10.1007/s10462-022-10296-0
25. Khishe, M., & Mosavi, M. R. (2020). Chimp optimization algorithm. Expert systems with applications, 149, 113338. DOI:10.1016/j.eswa.2020.113338
2. Chen, K., Huang, C., & He, J. (2016). Fault detection, classification and location for transmission lines and distribution systems: a review on the methods. High voltage, 1(1), 25-33. DOI:10.1049/HVE.2016.0005
3. Mishra, D. P., & Ray, P. (2018). Fault detection, location and classification of a transmission line. Neural Computing and Applications, 30, 1377-1424. DOI:10.1007/s00521-017-3295-y
4. Singh, M. R., Chopra, T., Singh, R., & Chopra, T. (2015). Fault classification in electric power transmission lines using support vector machine. Int. J. Innov. Res. Sci. Technol, 1(12), 388-400. Corpus ID: 18709549
5. Guo, M. F., Yang, N. C., & Chen, W. F. (2019). Deep-learning-based fault classification using Hilbert–Huang transform and convolutional neural network in power distribution systems. IEEE Sensors Journal, 19(16), 6905-6913. DOI: 10.1109/JSEN.2019.2913006
6. Dhibi, K., Fezai, R., Mansouri, M., Trabelsi, M., Kouadri, A., Bouzara, K., ... & Nounou, M. (2020). Reduced kernel random forest technique for fault detection and classification in grid-tied PV systems. IEEE Journal of Photovoltaics, 10(6), 1864-1871. DOI: 10.1109/JPHOTOV.2020.3011068
7. Al Kharusi, K., El Haffar, A., & Mesbah, M. (2022). Fault detection and classification in transmission lines connected to inverter-based generators using machine learning. Energies, 15(15), 5475. https://doi.org/10.3390/en15155475
8. Pranav, M. S., Karthik, C., Kavitha, D., Vishal, K., Tarun, J., & Vanitha, V. (2018, May). Fault Detection and Classification in Three Phase Transmission Lines using Signal Processing. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 347-350). IEEE. DOI:10.1109/RTEICT42901.2018.9012246
9. Omar, A. M. S., Osman, M. K., Ibrahim, M. N., Hussain, Z., & Abidin, A. F. (2020). Fault classification on transmission line using LSTM network. Indonesian Journal of Electrical Engineering and Computer Science, 20(1), 231-238.
DOI: http://doi.org/10.11591/ijeecs.v20.i1.pp231-238
10. Elnozahy, A., Sayed, K., & Bahyeldin, M. (2019, October). Artificial neural network based fault classification and location for transmission lines. In 2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE) (pp. 140-144). IEEE. DOI:10.1109/CPERE45374.2019.8980173
11. Zhang, L., Zhao, Z., Zhang, D., Luo, C., & Li, C. (2022). Particle swarm optimization pattern recognition neural network for transmission lines faults classification. Intelligent Data Analysis, 26(1), 189-203. DOI:10.3233/IDA-205695
12. de Souza Gomes, A., Costa, M. A., de Faria, T. G. A., & Caminhas, W. M. (2013). Detection and classification of faults in power transmission lines using functional analysis and computational intelligence. IEEE Transactions on Power Delivery, 28(3), 1402-1413. DOI:10.1109/TPWRD.2013.2251752
13. Najafzadeh, M., Pouladi, J., Daghigh, A., Beiza, J., & Abedinzade, T. (2024). Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning Algorithms. International Journal of Computational Intelligence Systems, 17(1), 49. DOIhttps://doi.org/10.1007/s44196-024-00434-7
14. Dash, P. K., Samantaray, S. R., & Panda, G. (2006). Fault classification and section identification of an advanced series-compensated transmission line using support vector machine. IEEE transactions on power delivery, 22(1), 67-73. DOI:10.1109/TPWRD.2006.876695
15. Gomathy, V., & Selvaperumal, S. (2016). Fault detection and classification with optimization techniques for a three-phase single-inverter circuit. Journal of Power Electronics, 16(3), 1097-1109. DOI:10.6113/JPE.2016.16.3.1097
16. Ahanch, M., Asasi, M. S., & McCann, R. (2021, April). Transmission lines fault detection, classification and location considering wavelet support vector machine with Harris Hawks optimization algorithm to improve the SVR training. In 2021 8th International Conference on Electrical and Electronics Engineering (ICEEE) (pp. 155-160). IEEE. DOI:10.1109/ICEEE52452.2021.9415887
17. Jawad, R. S., & Abid, H. (2022). Fault detection in HVDC system with gray wolf optimization algorithm based on artificial neural network. Energies, 15(20), 7775. https://doi.org/10.3390/en15207775
18. Jawad, R. S., & Abid, H. (2023). Fault detection and classification for voltage source converter-high voltage systems by using different swarm optimization algorithms-based neural network. Engineered Science, 23(2), 884.DOI:10.30919/es884
19. Ghashghaei, S., & Akhbari, M. (2021). Fault detection and classification of an HVDC transmission line using a heterogenous multi‐machine learning algorithm. IET Generation, Transmission & Distribution, 15(16), 2319-2332.https://doi.org/10.1049/gtd2.12180
20. Kanwal, S., & Jiriwibhakorn, S. (2023). Artificial intelligence based faults identification, classification, and localization techniques in transmission lines-a review. IEEE Latin America Transactions, 21(12), 1291-1305. DOI:10.1109/TLA.2023.10305233
21. Ray, P., & Mishra, D. P. (2016). Support vector machine based fault classification and location of a long transmission line. Engineering science and technology, an international journal, 19(3), 1368-1380. https://doi.org/10.1016/j.jestch.2016.04.001
22. Kumar, A. N., Kumar, M. S., Ramesha, M., Gururaj, B., & Srikanth, A. (2021). Support vector machine based fault section identification and fault classification scheme in six phase transmission line. IAES International Journal of Artificial Intelligence, 10(4), 1019. DOI: http://doi.org/10.11591/ijai.v10.i4.pp1019-1024
23. Fei, C., & Qin, J. (2021). Fault location after fault classification in transmission line using voltage amplitudes and support vector machine. Russian Electrical Engineering, 92(2), 112-121. DOI:10.1016/j.epsr.2018.02.005
24. Shakiba, F. M., Azizi, S. M., Zhou, M., & Abusorrah, A. (2023). Application of machine learning methods in fault detection and classification of power transmission lines: a survey. Artificial Intelligence Review, 56(7), 5799-5836. https://doi.org/10.1007/s10462-022-10296-0
25. Khishe, M., & Mosavi, M. R. (2020). Chimp optimization algorithm. Expert systems with applications, 149, 113338. DOI:10.1016/j.eswa.2020.113338