This work proposes a unique machine-learning method based on optimization for the categorization and identification of defects in transmission lines. The novel hybrid optimization algorithm termed as the Chimpanzee inherited Squirrel search strategy (CI-SSS) optimization technique is used in the proposed approach. The proposed CI-SSS algorithm inherits the concept of chimps and squirrels in attaining their food with remarkable intelligence. The proposed approach involves optimizing the SVM's parameters to improve the proposed model's accuracy in identifying and classifying transmission line faults. The accuracy and error metrics of the suggested method is studied. The accuracy CI-SSS is 98.82%, which is 11.35%, 5.41%, 0.84%, and 9.55% higher than methods, like GWO, DA, SSA, and CH, correspondingly. Similarly, the measure of MAE using the proposed CI-SSS-based SVM model is 0.0104, which is 84.5%, 87.7%, 85.73%, and 62.85% finer than the traditional methods, namely GWO, DA, SSA, and CH, respectively. Hence, the suggested strategy offers improved performance in classifying and detecting transmission line faults.

Open Research

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

References

1A. Yadav and A. Swetapadma, “Enhancing the Performance of Transmission Line Directional Relaying, Fault Classification and Fault Location Schemes Using Fuzzy Inference System,” IET Generation Transmission and Distribution 9, no. 6 (2015): 580–591.
10.1049/iet-gtd.2014.0498
Web of Science® Google Scholar
2B. Bhalja and R. P. Maheshwari, “Wavelet-Based Fault Classification Scheme for a Transmission Line Using a Support Vector Machine,” Electric Power Components & Systems 36, no. 10 (2008): 1017–1030.
10.1080/15325000802046496
Web of Science® Google Scholar
3H. A. Illias, M. M. Lim, A. H. Abu Bakar, H. Mokhlis, S. Ishak, and M. D. M. Amir, “Classification of Abnormal Location in Medium Voltage Switchgears Using Hybrid Gravitational Search Algorithm-Artificial Intelligence,” PLoS One 16, no. 7 (2021): e0253967.
10.1371/journal.pone.0253967
CAS PubMed Web of Science® Google Scholar
4H. A. Illias, K. C. Chan, and H. Mokhlis, “Hybrid Feature Selection–Artificial Intelligence–Gravitational Search Algorithm Technique for Automated Transformer Fault Determination Based on Dissolved Gas Analysis,” IET Generation Transmission and Distribution 14, no. 8 (2020): 1575–1582.
10.1049/iet-gtd.2019.1189
Web of Science® Google Scholar
5J. C. A. Freire, A. R. G. Castro, M. S. Homci, B. S. Meiguins, and J. M. De Morais, “Transmission Line Fault Classification Using Hidden Markov Models,” IEEE Access 7 (2019): 113499–113510.
10.1109/ACCESS.2019.2934938
Web of Science® Google Scholar
6S. R. Samantaray, P. K. Dash, and G. Panda, “ Fault Classification and Ground Detection Using Support Vector Machine,” in TENCON 2006–2006 IEEE Region 10 Conference (Hong Kong, China: IEEE, 2006), 1–3.
10.1109/TENCON.2006.344216
Google Scholar
7M. T. Hagh, K. Razi, and H. Taghizadeh, “ Fault Classification and Location of Power Transmission Lines Using Artificial Neural Network,” in 2007 International Power Engineering Conference (IPEC 2007) (Singapore: IEEE, 2007), 1109–1114.
Google Scholar
8J. Sadeh, N. Hadjsaid, A. M. Ranjbar, and R. Feuillet, “Accurate Fault Location Algorithm for Series Compensated Transmission Lines,” IEEE Transactions on Power Delivery 15, no. 3 (2000): 1027–1033.
10.1109/61.871370
Web of Science® Google Scholar
9Z. Zhang, D. Zhang, and R. C. Qiu, “Deep Reinforcement Learning for Power System Applications: An Overview,” CSEE Journal of Power and Energy Systems 6, no. 1 (2019): 213–225.
Web of Science® Google Scholar
10P. Ray and D. P. Mishra, “Support Vector Machine Based Fault Classification and Location of a Long Transmission Line,” Engineering Science and Technology 19, no. 3 (2016): 1368–1380.
Google Scholar
11S. Das, S. Santoso, A. Gaikwad, and M. Patel, “Impedance-Based Fault Location in Transmission Networks: Theory and Application,” IEEE Access 2 (2014): 537–557.
10.1109/ACCESS.2014.2323353
Web of Science® Google Scholar
12B. Mahamedi and J. G. Zhu, “Fault Classification and Faulted Phase Selection Based on the Symmetrical Components of Reactive Power for Single-Circuit Transmission Lines,” IEEE Transactions on Power Delivery 28, no. 4 (2013): 2326–2332.
10.1109/TPWRD.2013.2265711
Web of Science® Google Scholar
13S. R. Fahim, Y. Sarker, S. K. Sarker, M. R. I. Sheikh, and S. K. Das, “Self Attention Convolutional Neural Network With Time Series Imaging Based Feature Extraction for Transmission Line Fault Detection and Classification,” Electric Power Systems Research 187 (2020): 106437.
10.1016/j.epsr.2020.106437
Web of Science® Google Scholar
14M. Jain, V. Singh, and A. Rani, “A Novel Nature-Inspired Algorithm for Optimization: Squirrel Search Algorithm,” Swarm and Evolutionary Computation 44 (2019): 148–175.
10.1016/j.swevo.2018.02.013
Web of Science® Google Scholar
15M. Khishe and M. R. Mosavi, “Chimp Optimization Algorithm,” Expert Systems with Applications 149 (2020): 113338.
10.1016/j.eswa.2020.113338
Web of Science® Google Scholar
16P. D. Raval and A. S. Pandya, “A Hybrid PSO-ANN-Based Fault Classification System for EHV Transmission Lines,” IETE Journal of Research 68, no. 4 (2022): 3086–3099.
10.1080/03772063.2020.1754299
Web of Science® Google Scholar
17B. Y. Vyas, R. P. Maheshwari, and B. Das, “Versatile Relaying Algorithm for Detection and Classification of Fault on Transmission Line,” Electric Power Systems Research 192 (2021): 106913.
10.1016/j.epsr.2020.106913
Web of Science® Google Scholar
18P. Rai, N. D. Londhe, and R. Raj, “Fault Classification in Power System Distribution Network Integrated With Distributed Generators Using CNN,” Electric Power Systems Research 192 (2021): 106914.
10.1016/j.epsr.2020.106914
Web of Science® Google Scholar
19P. Rajesh, R. Kannan, J. Vishnupriyan, and B. Rajani, “Optimally Detecting and Classifying the Transmission Line Fault in Power System Using Hybrid Technique,” ISA Transactions 130 (2022): 253–264.
10.1016/j.isatra.2022.03.017
CAS PubMed Web of Science® Google Scholar
20C. D. Prasad, M. Biswal, and P. K. Nayak, “Wavelet Operated Single Index Based Fault Detection Scheme for Transmission Line Protection With Swarm Intelligent Support,” Energy Systems 12 (2021): 373–392.
10.1007/s12667-019-00373-9
Google Scholar
21S. K. Shukla, E. Koley, and S. Ghosh, “Grey Wolf Optimization-Tuned Convolutional Neural Network for Transmission Line Protection With Immunity Against Symmetrical and Asymmetrical Power Swing,” Neural Computing and Applications 32 (2020): 17059–17076.
10.1007/s00521-020-04938-z
Web of Science® Google Scholar
22E. Aker, M. L. Othman, V. Veerasamy, I. B. Aris, N. I. A. Wahab, and H. Hizam, “Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier,” Energies 13, no. 1 (2020): 243.
10.3390/en13010243
Web of Science® Google Scholar
23M. O. F. Goni, M. Nahiduzzaman, M. S. Anower, et al., “Fast and Accurate Fault Detection and Classification in Transmission Lines Using Extreme Learning Machine,” E-Prime-Advances in Electrical Engineering, Electronics and Energy 3 (2023): 100107.
10.1016/j.prime.2023.100107
Google Scholar
24M. Najafzadeh, J. Pouladi, A. Daghigh, J. Beiza, and T. Abedinzade, “Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning Algorithms,” International Journal of Computational Intelligence Systems 17, no. 1 (2024): 49.
10.1007/s44196-024-00434-7
Web of Science® Google Scholar
25S. Heo and J. H. Lee, “Fault Detection and Classification Using Artificial Neural Networks,” IFAC-PapersOnLine 51, no. 18 (2018): 470–475.
10.1016/j.ifacol.2018.09.380
Google Scholar
26S. Belagoune, N. Bali, A. Bakdi, B. Baadji, and K. Atif, “Deep Learning Through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems,” Measurement 177 (2021): 109330.
10.1016/j.measurement.2021.109330
Web of Science® Google Scholar
27Y. Q. Chen, O. Fink, and G. Sansavini, “Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction,” IEEE Transactions on Industrial Electronics 65, no. 1 (2017): 561–569.
10.1109/TIE.2017.2721922
Web of Science® Google Scholar
28V. Malathi and N. S. Marimuthu, “ Multi-Class Support Vector Machine Approach for Fault Classification in Power Transmission Line,” in 2008 IEEE International Conference on Sustainable Energy Technologies (Singapore: IEEE, 2008), 67–71.
10.1109/ICSET.2008.4746974
Google Scholar
29O. A. Youssef, “ An Optimised Fault Classification Technique Based on Support-Vector-Machines,” in 2009 IEEE/PES Power Systems Conference and Exposition (Seattle, WA: IEEE, 2009), 1–8.
10.1109/PSCE.2009.4839949
Google Scholar
30V. P. Sakthivel, M. Suman, and P. D. Sathya, “Combined Economic and Emission Power Dispatch Problems Through Multi-Objective Squirrel Search Algorithm,” Applied Soft Computing 100 (2021): 106950.
10.1016/j.asoc.2020.106950
Web of Science® Google Scholar
31M. Khishe, M. Nezhadshahbodaghi, M. R. Mosavi, and D. Martín, “A Weighted Chimp Optimization Algorithm,” IEEE Access 9 (2021): 158508–158539.
10.1109/ACCESS.2021.3130933
Web of Science® Google Scholar
32S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software 69 (2014): 46–61.
10.1016/j.advengsoft.2013.12.007
Web of Science® Google Scholar
33H. Jia, K. Sun, W. Zhang, and X. Leng, “An Enhanced Chimp Optimization Algorithm for Continuous Optimization Domains,” Complex & Intelligent Systems 8 (2021): 1–18.
Web of Science® Google Scholar
34S. Mirjalili, “Dragonfly Algorithm: A New Meta-Heuristic Optimization Technique for Solving Single-Objective, Discrete, and Multi-Objective Problems,” Neural Computing and Applications 27 (2016): 1053–1073.
10.1007/s00521-015-1920-1
Web of Science® Google Scholar
35A. Saha, P. Dey, A. Bhattacharya, and B. Marungsri, “SSA—A New Metaheuristic Algorithm for Solving Transient Stability Constrained Optimal Power Flow” (2019).
Google Scholar

Volume36, Issue3

March 2025

e70034

Fault Classification and Detection in Transmission Lines by Hybrid Algorithm Associated Support Vector Machine

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Fault Classification and Detection in Transmission Lines by Hybrid Algorithm Associated Support Vector Machine

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Open Research

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