Optimizing Fault Identification in Power Distribution Systems by the Combination of SVM and Deep Learning Models

Articles in Press

Document Type : Research paper

Authors

Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, India.

Abstract

Maintaining electrical grid stability and reliability requires the rapid diagnosis and classification of faults in power distribution systems. This study presents a hybrid model that integrates deep learning with support vector machine (SVM) methodologies to classify distribution system faults. In the proposed approach, feature extraction is performed using a convolutional neural network (CNN), and an SVM classifier is employed to identify fault patterns and establish generic fault classifications. The hybrid model is trained and evaluated using an extensive dataset comprising power distribution system fault currents under various fault types and conditions. The integration of deep learning feature extraction with SVM classification enhances fault classification effectiveness. This study aims to contribute to the overall improvement of distribution system reliability, reduction of downtime, and more efficient grid management. To achieve this, PSCAD software is utilized to simulate faults and collect images of three-phase fault current data. Initially, the fault classification problem is addressed using four pre-trained CNN models, with the collected images serving as input data. The hybrid model consists of two distinct components: an SVM block, known for its efficient and precise data classification capabilities, and a CNN block, specifically designed for feature extraction. In the MATLAB environment, a combination of four pre-trained CNN models—AlexNet, SqueezeNet, GoogLeNet, and ResNet-18—are utilized in conjunction with an SVM to create hybrid models. The hybrid SqueezeNet-SVM model has demonstrated exceptional performance, achieving an accuracy rate of 99.95%, a precision rate of 99.98%, a sensitivity rate of 99.6%, and a specificity rate of 99.7%.

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References

  1. L. C. Acácio, P. A. Guaracy, T. O. Diniz, D. R. Araujo, and L. R. Araujo, “Evaluation of the impact of different neural network structure and data input on fault detection,” in 2017 IEEE PES Innovative Smart Grid Technol. Conf.-Lat. Am., pp. 1–5, IEEE, 2017.
  2. M. Bjelic, B. Brkovi´ c, M. Žarkovi´ c, and T. Miljkovi´ c, “Fault´ detection in a power transformer based on reverberation time,” Int. J. Electr. Power Energy Syst., vol. 137, p. 107825, 2022.
  3. M. Khoshbouy, A. Yazdaninejadi, and T. G. Bolandi, “Transmission line adaptive protection scheme: A new fault detection approach based on pilot superimposed impedance,” Int. J. Electr. Power Energy Syst., vol. 137, p. 107826, 2022.
  4. J. Zheng, P. Li, K. Xu, X. Kong, C. Wang, J. Lin, and C. Zhang, “A distance protection scheme for hvdc transmission lines based on the steady-state parameter model,” Int. J. Electr. Power Energy Syst., vol. 136, p. 107658, 2022.
  5. A. Prasad and J. B. Edward, “Application of wavelet technique for fault classification in transmission systems,” Procedia Comput. Sci., vol. 92, pp. 78–83, 2016.
  6. P. Rajaraman, N. Sundaravaradan, R. Meyur, M. J. B. Reddy, and D. Mohanta, “Fault classification in transmission lines using wavelet multiresolution analysis,” IEEE Potentials, vol. 35, no. 1, pp. 38–44, 2016.
  7. G. Vikram Raju and N. Venkata Srikanth, “Mono ann module protection scheme and multi ann modules for fault location estimation for a six-phase transmission line using discrete wavelet transform,” J. Oper. Autom. Power Eng., vol. 12, no. 4, pp. 337–351, 2024.
  8. M. O. F. Goni, M. Nahiduzzaman, M. S. Anower, M. M. Rahman, M. R. Islam, M. Ahsan, J. Haider, and M. Shahjalal, “Fast and accurate fault detection and classification in transmission lines using extreme learning machine,” e-Prime Adv. Electr. Eng. Electron. Energy, vol. 3, p. 100107, 2023.
  9. M. He and D. He, “Deep learning based approach for bearing fault diagnosis,” IEEE Trans. Ind. Appl., vol. 53, no. 3, pp. 3057–3065, 2017.
  10. G. Tiwari and S. Saini, “Estimation of location and fault types detection using sequence current components in distribution cable system using ann,” in Mobile Radio Commun. 5G Net. Proc. Second 2021, pp. 119–128, Springer, 2022.
  11. A. Saber, A. Emam, and H. Elghazaly, “A backup protection technique for three-terminal multisection compound transmission lines,” IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 5653–5663, 2017.
  12. G. Tiwari and S. Saini, “Evaluation of transfer learning techniques for fault classification in radial distribution systems: A comparative study,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 11, pp. 627–633, 2023.
  13. X. Deng, R. Yuan, Z. Xiao, T. Li, and K. L. L. Wang, “Fault location in loop distribution network using svm technology,” Int. J. Electr. Power Energy Syst., vol. 65, pp. 254–261, 2015.
  14. M. B. Hessine, H. Jouini, and S. Chebbi, “Fault detection and classification approaches in transmission lines using artificial neural networks,” in MELECON 2014-2014 17th IEEE Mediterr. Electrotech. Conf., pp. 515–519, IEEE, 2014.
  15. Y. Liang, K.-J. Li, Z. Ma, and W.-J. Lee, “Multilabel classification model for type recognition of single-phase-toground fault based on knn-bayesian method,” IEEE Trans. Ind. Appl., vol. 57, no. 2, pp. 1294–1302, 2021.
  16. P. Chopra and S. K. Yadav, “Pca and feature correlation for fault detection and classification,” in 2015 IEEE Recent Adv. Intell. Comput. Syst., pp. 195–200, IEEE, 2015.
  17. A. A. M. Zin, M. Saini, M. W. Mustafa, A. R. Sultan, et al., “New algorithm for detection and fault classification on parallel transmission line using dwt and bpnn based on clarke’s transformation,” Neurocomputing, vol. 168, pp. 983– 993, 2015.
  18. G. Tiwari and S. Saini, “Radial power distribution system fault classification model based on anfis,” Int. J. Recent Innovation Trends Comput. Commun., vol. 11, no. 6, 2023.
  19. M. Shafiullah and M. A. Abido, “S-transform based ffnn approach for distribution grids fault detection and classification,” IEEE Access, vol. 6, pp. 8080–8088, 2018.
  20. T. Zheng, Y. Liu, Y. Yan, S. Xiong, T. Lin, Y. Chen, Z. Wang, and X. Jiang, “Rsspn: robust semi-supervised prototypical network for fault root cause classification in power distribution systems,” IEEE Trans. Power Delivery, vol. 37, no. 4, pp. 3282–3290, 2021.
  21. M. Dehghani, M. H. Khooban, and T. Niknam, “Fast fault detection and classification based on a combination of wavelet singular entropy theory and fuzzy logic in distribution lines in the presence of distributed generations,” Int. J. Electr. Power Energy Syst., vol. 78, pp. 455–462, 2016.
  22. M. Jamil, R. Singh, and S. K. Sharma, “Fault identification in electrical power distribution system using combined discrete wavelet transform and fuzzy logic,” J. Electr. Syste. Inf. Technol., vol. 2, no. 2, pp. 257–267, 2015.
  23. J. James, Y. Hou, A. Y. Lam, and V. O. Li, “Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks,” IEEE Trans. Smart Grid, vol. 10, no. 2, pp. 1694–1703, 2017.
  24. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nat., vol. 521, no. 7553, pp. 436–444, 2015.
  25. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017.
  26. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., pp. 1–9, 2015.
  27. S. Senemmar and J. Zhang, “Deep learning-based fault detection, classification, and locating in shipboard power systems,” in 2021 IEEE Electr. Ship Technol. Symp., pp. 1–6, IEEE, 2021.
  28. P. Rai, N. D. Londhe, and R. Raj, “Fault classification in power system distribution network integrated with distributed generators using cnn,” Electr. Power Syst. Res., vol. 192, p. 106914, 2021.
  29. N. N. Bon et al., “Fault identification, classification, and location on transmission lines using combined machine learning methods.,” Int. J. Eng. Technol. Innovation, vol. 12, no. 2, 2022.
  30. Y. Xi, W. Zhang, F. Zhou, X. Tang, Z. Li, X. Zeng, and P. Zhang, “Transmission line fault detection and classification based on sa-mobilenetv3,” Energy Rep., vol. 9, pp. 955–968, 2023.
  31. A. Moradzadeh, H. Teimourzadeh, B. Mohammadi-Ivatloo, and K. Pourhossein, “Hybrid cnn-lstm approaches for identification of type and locations of transmission line faults,” Int. J. Electr. Power Energy Syst., vol. 135, p. 107563, 2022.
  32. Y. Ma, D. Oslebo, A. Maqsood, and K. Corzine, “Dc fault detection and pulsed load monitoring using wavelet transform-fed lstm autoencoders,” IEEE J. Emerging Sel. Top. Power Electron., vol. 9, no. 6, pp. 7078–7087, 2020.
  33. Y. Ma, D. Oslebo, A. Maqsood, and K. Corzine, “Pulsedpower load monitoring for an all-electric ship: Utilizing the fourier transform data-driven deep learning approach,” IEEE Electrif. Mag., vol. 9, no. 1, pp. 25–35, 2021.
  34. M. N. I. Siddique, M. Shafiullah, S. Mekhilef, H. Pota, and M. Abido, “Fault classification and location of a pmuequipped active distribution network using deep convolution neural network (cnn),” Electr. Power Syst. Res., vol. 229, p. 110178, 2024.
  35. J. Zhu, L. Mu, D. Ma, and X. Zhang, “Faulty line identification method based on bayesian optimization for distribution network,” IEEE Access, vol. 9, pp. 83175–83184, 2021.
  36. P. Venkata, V. Pandya, and A. Sant, “Data mining and svm based fault diagnostic analysis in modern power system using time and frequency series parameters calculated from full-cycle moving window,” J. Oper. Autom. Power Eng., vol. 12, no. 3, pp. 206–214, 2024.
  37. O. W. Chuan, N. F. Ab Aziz, Z. M. Yasin, N. A. Salim, and N. A. Wahab, “Fault classification in smart distribution network using support vector machine,” Indones. J. Electr. Eng. Comput. Sci., vol. 18, no. 3, pp. 1148–1155, 2020.
  38. J. Li, Z. Weng, H. Xu, Z. Zhang, H. Miao, W. Chen, Z. Liu,
    X. Zhang, M. Wang, X. Xu, et al., “Support vector machines (svm) classification of prostate cancer gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study,” Eur. J. Radiol., vol. 98, pp. 61–67, 2018.
  39. F. Sultana, A. Sufian, and P. Dutta, “Advancements in image classification using convolutional neural network,” in 2018 Fourth Int. Conf. Res. Comput. Intell. Commun. Net., pp. 122–129, IEEE, 2018.
  40. L. Ali, F. Alnajjar, H. A. Jassmi, M. Gocho, W. Khan, and M. A. Serhani, “Performance evaluation of deep cnn-based crack detection and localization techniques for concrete structures,” Sens., vol. 21, no. 5, p. 1688, 2021.
  41. R. Andonie and A.-C. Florea, “Weighted random search for cnn hyperparameter optimization,” arXiv Prepr. arXiv:2003.13300, 2020.
  42. A. Ibrahim, F. Anayi, and M. Packianather, “New transfer learning approach based on cnn network for fault diagnosis,” Eng. Proc., vol. 4, 2022.
  43. M. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, B. C. Van Esesn, A. A. S. Awwal, and V. K. Asari, “The history began from alexnet: A comprehensive survey on deep learning approaches,” arXiv Prepr. arXiv:1803.01164, 2018.
  44. K. Sudha and P. Sujatha, “A qualitative analysis of googlenet and alexnet for fabric defect detection,” Int. J. Recent Technol. Eng., vol. 8, no. 1, pp. 86–92, 2019.
  45. B. Liu, Z.-F. Hao, and X.-W. Yang, “Nesting support vector machinte for muti-classification [machinte read machine],” in 2005 Int. Conf. Mach. Learn. Cybern., vol. 7, pp. 4220–4225, IEEE, 2005.
  46. F. N. Iandola, “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size,” arXiv Prepr. arXiv:1602.07360, 2016.
  47. F. N. Iandola, A. E. Shaw, R. Krishna, and K. W. Keutzer, “Squeezebert: What can computer vision teach nlp about efficient neural networks?,” arXiv Prepr. arXiv:2006.11316, 2020.
  48. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., pp. 770–778, 2016.
  49. M. Yin, X. Li, Y. Zhang, and S. Wang, “On the mathematical understanding of resnet with feynman path integral,” arXiv Prepr. arXiv:1904.07568, 2019.
  50. Q. Wu, T. Gao, Z. Lai, and D. Li, “Hybrid svm-cnn classification technique for human–vehicle targets in an automotive lfmcw radar,” Sens., vol. 20, no. 12, p. 3504, 2020.
  51. F. Islam, K. Prakash, K. A. Mamun, A. Lallu, and H. R. Pota, “Aromatic network: A novel structure for power distribution system,” IEEE Access, vol. 5, pp. 25236–25257, 2017.
  52. “The c parameter in support vector machines | baeldung on computer science.” https://www.baeldung.com/ cs/ml-svm-c-parameter. Accessed: 2024-08-04.
Journal of Operation and Automation in Power Engineering

Articles in Press, Corrected Proof
Available Online from 23 November 2024

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  • Receive Date: 30 October 2023
  • Revise Date: 19 August 2024
  • Accept Date: 20 August 2024
  • First Publish Date: 23 November 2024

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