Empirical Mode Decomposition and Optimization Assisted ANN Based Fault Classification Schemes for Series Capacitor Compensated Transmission Line

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Faculty of Engineering & Technology, Annamalai University, Annamalainagar, 608002, Tamil Nadu, India.

2 Department of Electrical & Electronics Engineering, Sagi Rama Krishnam Raju Engineering College Bhimavaram-534202, Andhra Pradesh, India

Abstract

This paper presents two intelligent classifier schemes for classifying the faults in a series capacitor compensated transmission line (SCCTL). The first proposed intelligent classifier scheme is a particle swarm optimization-assisted artificial neural network (PSO-ANN). The second, proposed one is a teaching-learning optimization-assisted artificial neural network (TLBO-ANN). For each type of fault, the 3-phase current signals are acquired at the sending end and processed through empirical mode decomposition (EMD), to decompose into six intrinsic mode functions. The neighborhood component analysis is used to extract the best feature intrinsic mode functions. From the identified best feature intrinsic mode functions, the energy of each phase of the line is computed. The energy of each phase is fed as inputs for both PSO-ANN and TLBO-ANN classifiers. The practicability of the proposed intelligent classifier schemes has been tested on a 500$\,kV$, 50$\,Hz$, and 300$\,km$ long line with a midpoint series capacitor using MATLAB/Simulink Software. The results demonstrate that the classifier schemes are able to accurately classify faults in less than a half-cycle. Furthermore, the efficacy of the proposed intelligent classifier schemes has been evaluated using Performance Indices including Kappa Statistics, Mean Absolute Error, Root Mean Square Error, Precision, Recall, F-measure, and Receiver Operating Characteristics. From the results of Performance Indices, it is concluded that the proposed TLBO-based artificial neural network classifier outperforms the PSO-based artificial neural network classifier. Finally, the efficacies of proposed intelligent classifier schemes are compared to existing approaches.

Keywords

Main Subjects

References

  1. T. Hoq, J. Wang, and N. Taylor, “Review of recent developments in distance protection of series capacitor compensated lines,” Electr. Power Syst. Res., vol. 190, p. 106831, 2021.
  2. Vyas, R.P. Maheshwari, and B. Das, “Protection of series compensated transmission line: issues and state of art,” Electr. Power Syst. Res., vol. 107, pp. 93–108, 2014.
  3. Shehab-Eldin and P. McLaren, “Travelling wave distance protection-problem areas and solutions,” IEEE Trans. Power Deliv., vol. 3, no. 3, pp. 894–902, 1988.
  4. A. Youssef, “Combined fuzzy-logic waveletbased fault classification technique for power system relaying,” IEEE Trans. Power Deliv., vol. 19, no. 2, pp. 582–589, 2004.
  5. A. Girgis and M.B. Johns, “A hybrid expert system for faulted section identification, fault type classification and selection of fault location algorithms,” IEEE Power Eng. Rev., vol. 9, no. 4, pp. 56–57, 1989.
  6. -A. Jiang, C.-S. Chen, and C.-W. Liu, “A new protection scheme for fault detection, direction discrimination, classification, and location in transmission lines,” IEEE Trans. Power Deliv., vol. 18, no. 1, pp. 34–42, 2003.
  7. Purushothama, A. Narendranath, D. Thukaram, and K. Parthasarathy, “Ann applications in fault locators,” Int. J. Electr. Power Energy Syst., vol. 23, no. 6, pp. 491–506, 2001.
  8. Mulltilin, “D90 plus line distance protection system,” in Instruction manual, 2012.
  9. B. Roberts and E.O. Schweitzer III, “Fault identification system for use in protective relays for power transmission lines,” May 7 1996. US Patent 5,515,227.
  10. Upendar, C.P. Gupta, G.K. Singh, and G. Ramakrishna, “Pso and ann-based fault classification for protective relaying,” IET Gener. Transm. Distrib., vol. 4, no. 10, pp. 1197–1212, 2010.
  11. M. Taheri, H. Seyedi, and B. Mohammadi-ivatloo, “Dt-based relaying scheme for fault classification in transmission lines using modp,” IET Gener. Transm. Distrib., vol. 11, no. 11, pp. 2796–2804, 2017.
  12. D. Raval and A.S. Pandya, “A hybrid pso-ann-based fault classification system for ehv transmission lines,” IETE J. Res., vol. 68, no. 4, pp. 3086–3099, 2022.
  13. R. Babu and B.J. Mohan, “Fault classification in power systems using emd and svm,” Ain Shams Eng. J., vol. 8, no. 2, pp. 103–111, 2017.
  14. Malik and R. Sharma, “Emd and ann based intelligent fault diagnosis model for transmission line,” J. Intell. Fuzzy Syst., vol. 32, no. 4, pp. 3043–3050, 2017.
  15. Y. Vyas, R. Maheshwari, and B. Das, “Versatile relaying algorithm for detection and classification of fault on transmission line,” Electr. Power Syst. Res., vol. 192, p. 106913, 2021.
  16. K. Mishra, A. Yadav, and M. Pazoki, “A novel fault classification scheme for series capacitor compensated transmission line based on bagged tree ensemble classifier,” IEEE Access, vol. 6, pp. 27373–27382, 2018.
  17. Bhasker, M. Tripathy, A. Agrawal, and A. Mishra, “Differential protection of ispst using chebyshev neural network,” J. Oper. Autom. Power Eng., vol. 11, no. 2, pp. 123–129, 2023.
  18. Venkata, V. Pandya, and A. Sant, “Data mining model based differential microgrid fault classification using svm considering voltage and current distortions,” J. Oper. Autom. Power Eng., vol. 11, no. 3, pp. 162– 172, 2023.
  19. Taherian, I. Nazer, E. Razavi, S. Goldani, M. Farshad, and M. Aghaebrahimi, “Application of an improved neural network using cuckoo search algorithm in short-term electricity price forecasting under competitive power markets,” J. Oper. Autom. Power Eng., vol. 1, no. 2, pp. 136–146, 2007.
  20. 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,” in Adv. Electr. Electron. Eng., vol. 3, p. 100107, 2023.
  21. Gutierrez-Rojas, I.T. Christou, D. Dantas, A. Narayanan, P.H. Nardelli, and Y. Yang, “Performance evaluation of machine learning for fault selection in power transmission lines,” Knowl. Inf. Syst., vol. 64, no. 3, pp. 859–883, 2022.
  22. Bhatnagar, A. Yadav, and A. Swetapadma, “Enhancing the resiliency of transmission lines using extreme gradient boosting against faults,” Electr. Power Syst. Res., vol. 207, p. 107850, 2022.
  23. Pang, D. Yang, R. Teng, B. Zhou, and C. Xu, “A deep learning based multiple signals fusion architecture for power system fault diagnosis,” Sustain. Energy, Grids Netw., vol. 30, p. 100660, 2022.
  24. M. Shakiba, M. Shojaee, S.M. Azizi, and M. Zhou, “Generalized fault diagnosis method of transmission lines using transfer learning technique,” Neurocomputing, vol. 500, pp. 556–566, 2022.
  25. E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.-C. Yen, C.C. Tung, and H.H. Liu, “The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. A: Math. Phys. Eng. Sci., vol. 454, no. 1971, pp. 903–995, 1998.
  26. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. Int. Jt. Conf. Neural Netw., vol. 4, pp. 1942–1948, IEEE, 1995.
  27. K. Simpson, Artificial neural systems: foundations, paradigms, applications, and implementations. McGraw-Hill, Inc., 1991.
  28. V. Rao, V.J. Savsani, and D. Vakharia, “Teaching– learning-based optimization: a novel method for constrained mechanical design optimization problems,” Comput. Aided Des., vol. 43, no. 3, pp. 303–315, 2011.
  29. Vinayagam, V. Veerasamy, P. Radhakrishnan, M. Sepperumal, and K. Ramaiyan, “An ensemble approach of classification model for detection and classification of power quality disturbances in pv integrated microgrid network,” Appl. Soft Comput., vol. 106, p. 107294, 2021.
  30. Yang, K. Wang, and W. Zuo, “Neighborhood component feature selection for high-dimensional data.,” J. Comput., vol. 7, no. 1, pp. 161–168, 2012.
Journal of Operation and Automation in Power Engineering
Volume 13, Issue 1
January 2025
Pages 52-73

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History

  • Receive Date: 23 January 2023
  • Revise Date: 28 April 2023
  • Accept Date: 01 June 2023
  • First Publish Date: 16 September 2023

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