Mohammed, Q., Al-Taei, A. (2023). Distribution Power System Protected by Petersen Coil: Detection of Single Line to Ground Fault Using Deep Learning. Alustath, 28(2), 58-66. doi: 10.33899/rengj.2023.137579.1223
Qusay Muayad Mohammed; Abdulghani Abdulrazaq Al-Taei. "Distribution Power System Protected by Petersen Coil: Detection of Single Line to Ground Fault Using Deep Learning". Alustath, 28, 2, 2023, 58-66. doi: 10.33899/rengj.2023.137579.1223
Mohammed, Q., Al-Taei, A. (2023). 'Distribution Power System Protected by Petersen Coil: Detection of Single Line to Ground Fault Using Deep Learning', Alustath, 28(2), pp. 58-66. doi: 10.33899/rengj.2023.137579.1223
Mohammed, Q., Al-Taei, A. Distribution Power System Protected by Petersen Coil: Detection of Single Line to Ground Fault Using Deep Learning. Alustath, 2023; 28(2): 58-66. doi: 10.33899/rengj.2023.137579.1223

Distribution Power System Protected by Petersen Coil: Detection of Single Line to Ground Fault Using Deep Learning

Al-Rafidain Engineering Journal (AREJ)
Volume 28, Issue 2, September 2023, Pages 58-66    PDF (1 M)
Document Type: Research Paper
DOI: 10.33899/rengj.2023.137579.1223
Authors
Qusay Muayad Mohammed* ; Abdulghani Abdulrazaq Al-Taei
Electrical Engineering Department, Collage of Engineering, University of Mosul, Mosul, Iraq
Abstract
In all fault detection techniques, fault signal feature extraction is crucial and challenging. Convolutional neural network and continuous wavelet transform (CNN)-the based technique of SLGF detection in distribution power system protected by Petersen coil proposed in this paper. By using the CWT on the zero-sequence current signals of the faulty feeder and healthy feeders, time-frequency RGB scale images acquired. A few RGB scale pictures under different types of faults circumstances, which will extract characteristics of RGB scale image adaptively, trained that which is CNN. A trained CNN could extract features and detect faulty feeder simultaneously. The distribution power system protected by Petersen coil simulated in MATLAB simulated and record the Zero Sequence Current ZSC and analyzed it by Orange big mining tool. The efficacy and the performance the suggested method for detecting faulty feeders are compared and confirmed under various faults scenarios, two methods for identifying faulty feeders on conventional machine learning and artificial feature extraction for comparison, concluded that The CNN best to detected the fault in different condition, for Test 1and Test 2 were classification accuracy 100%, and Test 3 was 99.5%, and Test 4 was 70.9%.
Keywords
Single Line to Ground Fault (SLGF); faulty feeder detection; Petersen coil; convolutional neural network; wavelet transform
References
  1. Chen, P. D. S. Assala, Y. Cai, and P. Yang, “Intelligent transient overvoltages location in distribution systems using wavelet packet decomposition and general regression neural networks,” IEEE Trans. Industr. Inform., vol. 12, no. 5, pp. 1726–1735, 2016.
  2. Wang, J. Zhou, Z. Li, Z. Dong, and Y. Xu, “Discriminant-analysis-based single-phase earth fault protection using improved PCA in distribution systems,” IEEE Trans. Power Deliv., vol. 30, no. 4, pp. 1974–1982, 2015.
  3. Ji, Q. Pang, and X. Liu, “Study on fault line detection based on genetic artificial neural network in compensated distribution system,” in 2006 IEEE International Conference on Information Acquisition, 2006.
  4. Zeng et al., “A novel single phase grounding fault protection scheme without threshold setting for neutral ineffectively earthed power systems,” CSEE J. Power Energy Syst., vol. 2, no. 3, pp. 73–81, 2016.
  5. I. Elkalashy, A. M. Elhaffar, T. A. Kawady, N. G. Tarhuni, and M. Lehtonen, “Bayesian selectivity technique for earth fault protection in medium-voltage networks,” IEEE Trans. Power Deliv., vol. 25, no. 4, pp. 2234–2245, 2010.
  6. Shao, L. Wang, and H. Zhang, “A fault line selection method for small current grounding system based on big data,” in 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2016.
  7. Wang et al., “Faulty feeder detection of single phase-earth fault using grey relation degree in resonant grounding system,” IEEE Trans. Power Deliv., vol. 32, no. 1, pp. 55–61, 2017.
  8. 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 Trans. Power Deliv., vol. 20, no. 1, pp. 397–407, 2005.
  9. Malla, W. Coburn, K. Keegan, and X.-H. Yu, “Power system fault detection and classification using wavelet transform and artificial neural networks,” in Advances in Neural Networks – ISNN 2019, Cham: Springer International Publishing, 2019, pp. 266–272.
  10. The illustrated wavelet transform handbook: Introductory theory and applications in science, engineering, medicine and finance. .
  11. Gu et al., “Recent advances in convolutional neural networks,” arXiv [cs.CV], 2015.
  12. Chen, S. Li, Q. Bai, J. Yang, S. Jiang, and Y. Miao, “Review of image classification algorithms based on convolutional neural networks,” Remote Sens. (Basel), vol. 13, no. 22, p. 4712, 2021.
  13. A. Mahmood and H. Mahmood, “Automatic triple-A segmentation of skin cancer images based on histogram classification,” AL-Rafdain Engineering Journal (AREJ), vol. 23, no. 5, pp. 31–42, 2015.
  14. Sufer Ali and H. Mohammed Hussein, “An efficient algorithm for eye detection in faces images,” AL-Rafdain Engineering Journal (AREJ), vol. 23, no. 1, pp. 23–29, 2015.
  15. Al-Mokhtar, F. Ibraheem, and H. Al-Layla, “A review of digital image fusion and its application,” Al-Rafidain Engineering Journal (AREJ), vol. 26, no. 2, pp. 309–322, 2021.
  16. Kavitha, B. Sandhya, and B. Thirumala, “Evaluation of Distance Measures for Feature based Image Registration using AlexNet,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 10, 2018.
  17. -F. Guo, X.-D. Zeng, D.-Y. Chen, and N.-C. Yang, “Deep-learning-based earth fault detection using continuous wavelet transform and convolutional neural network in resonant grounding distribution systems,” IEEE Sens. J., vol. 18, no. 3, pp. 1291–1300, 2018.
  18. Wang, “AdaBoost for feature selection, classification and its relation with SVM, A review,” Phys. Procedia, vol. 25, pp. 800–807, 2012.
  19. Tiantian and Z. Hongwei, “Large scale classification with local diversity AdaBoost SVM algorithm,” Journal of Systems Engineering and Electronics, vol. 20, pp. 1344–1350, 2009.
  20. Wang, X. Xiong, N. Zhou, Z. Li, and W. Wang, “Early warning method for transmission line galloping based on SVM and AdaBoost bi-level classifiers,” IET Gener. Transm. Distrib., vol. 10, no. 14, pp. 3499–3507, 2016.
  21. Qiang et al., “SqueezeNet and fusion network-based accurate fast fully convolutional network for hand detection and gesture recognition,” IEEE Access, vol. 9, pp. 77661–77674, 2021.
  22. Bisong and E. Bisong, “Training a Neural Network,” in Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, 2019, pp. 333–343.
Statistics
Article View: 167
PDF Download: 163


Journal Management System. Powered by ejournalplus.com