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Machine Learning Approaches in Power System Protection: A Review

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Proceedings of Integrated Intelligence Enable Networks and Computing

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Power system protection is a major element of the electrical power system, and power interruption due to the occurrence of faults in the system is not tolerable. A compressive review of techniques employed for fault diagnosis pedagogy in the power transmission system is discussed in this paper. Fault classification techniques by the applications of machine learning algorithm are introduced in this work. Classification of fault and perception techniques is mainly used artificial intelligence and signal processing. After the deliberation of techniques and conceptual employed for fault classifications, compressive review of fault classification techniques by using machine learning shown in tabular justification. This shows a corresponding application with results of the technique with comparative computing complexity in the system. This review work helps researchers to study and moderate effective techniques for fault diagnosis and elaborate use of machine learning in power system environment. Overall, a brief review of artificial intelligence and machine learning applications is advantages in power system protection schemes.

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References

  1. A. Raza, A. Benrabah, T. Alquthami, M. Akmal, A review of fault diagnosing methods in power transmission systems. Appl. Sci. 10(4) (2020). https://doi.org/10.3390/app10041312

  2. M. Mishra, R.R. Panigrahi, P.K. Rout, A combined mathematical morphology and extreme learning machine techniques based approach to micro-grid protection. Ain Shams Eng. J. 10(2), 307–318 (2019). https://doi.org/10.1016/j.asej.2019.03.011

    Article  Google Scholar 

  3. S.M. Miraftabzadeh, F. Foiadelli, M. Longo, M. Pasetti, A Survey of machine learning applications for power system analytics, in Proceedings—2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe EEEIC/I CPS Eur. 2019 (2019). https://doi.org/10.1109/eeeic.2019.8783340

  4. R.M. Lee, M.J. Assante, T. Conway, Analysis of the cyber attack on the ukrainian power grid defense use case. Electr. Inf. Shar. Anal. Cent. 36 (2016). [Online]. Available https://ics.sans.org/media/E-ISAC_SANS_Ukraine_DUC_5.pdf

  5. E. Hossain, I. Khan, F. Un-Noor, S.S. Sikander, M.S.H. Sunny, Application of big data and machine learning in smart grid, and associated security concerns: a review. IEEE Access 7(c), 13960–13988 (2019). https://doi.org/10.1109/access.2019.2894819

  6. O.A. Alimi, K. Ouahada, A.M. Abu-Mahfouz, A review of machine learning approaches to power system security and stability. IEEE Access 8, 113512–113531 (2020). https://doi.org/10.1109/ACCESS.2020.3003568

    Article  Google Scholar 

  7. D. Wang, X. Wang, Y. Zhang, L. Jin, Detection of power grid disturbances and cyber-attacks based on machine learning. J. Inf. Secur. Appl. 46, 42–52 (2019). https://doi.org/10.1016/j.jisa.2019.02.008

    Article  Google Scholar 

  8. M. Al Karim, J. Currie, T.-T. Lie, Distributed machine learning on dynamic power system data features to improve resiliency for the purpose of self-healing. Energies 13(13), 3494 (2020). https://doi.org/10.3390/en13133494

  9. A. Swetapadma, S. Member, A. Yadav, and S. Member, “A Novel Decision Tree Regression Based Fault Distance Estimation Scheme for Transmission Lines,” vol. 8977, no. c, 2016, https://doi.org/10.1109/tpwrd.2016.2598553

  10. A. Saber, A. Emam, H. Elghazaly, A backup protection technique for three—terminal multisection compound transmission lines. 3053(c) (2017). https://doi.org/10.1109/tsg.2017.2693322

  11. Y.G. Painthankar, S.R. Bhide, Fundermentals of Power System Protection (2003)

    Google Scholar 

  12. V.S. Agneeswaran, P. Tonpay, J. Tiwary, Paradigms for realizing machine learning algorithms. Big Data 1(4), 207–214 (2013). https://doi.org/10.1089/big.2013.0006

    Article  Google Scholar 

  13. S.K. Shukla, E. Koley, S. Ghosh, DC offset estimation-based fault detection in transmission line during power swing using ensemble of decision tree. (2018). https://doi.org/10.1049/iet-smt.2018.5071

  14. R. Godse, S. Bhat, mathematical morphology-based feature-extraction technique for detection and classification of faults on power transmission line. IEEE Access 8, 38459–38471 (2020). https://doi.org/10.1109/ACCESS.2020.2975431

    Article  Google Scholar 

  15. W.-Y. Loh, Classification algorithms and regression trees, in WIREs Data Mining and Knowledge Discovery, vol. 1 (Wiley, January/February 2011), pp. 14–23

    Google Scholar 

  16. J.J. Napiórkowski, A. Piotrowski, Artificial neural networks as an alternative to the Volterra series in rainfall-runoff modelling. Acta Geophys. Pol. 53(4), 459–472 (2005)

    Google Scholar 

  17. T. Hastie, R. Tibshirani, J. Friedman, in The Element of Statistical Learning. Data Mining, Inference, and Prediction. Springer Series in Statistics (2008)

    Google Scholar 

  18. A. Yadav, Y. Dash, An overview of transmission line protection by artificial neural network: fault detection, fault classification, fault location, and fault direction discrimination. Adv. Artif. Neural Syst. 2014, 1–20 (2014). https://doi.org/10.1155/2014/230382

    Article  Google Scholar 

  19. R.A. Asherson, Fuzzy Sets, Decision Making, and Expert Systems. International Series in Management Science/Operations Research (Kluwer Academic Publishers, Boston/Dordrecht/Lancaster, 2005)

    Google Scholar 

  20. S.R. Samantaray, A systematic fuzzy rule based approach for fault classification in transmission lines. Appl. Soft Comput. J. 13(2), 928–938 (2013). https://doi.org/10.1016/j.asoc.2012.09.010

    Article  Google Scholar 

  21. J.-S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3) (1993)

    Google Scholar 

  22. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    Google Scholar 

  23. M. Chassis, An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis. i, 20713–20735 (2014). https://doi.org/10.3390/s141120713

  24. U.B. Parikh, B. Das, R. Maheshwari, Electrical Power and Energy Systems Fault classification technique for series compensated transmission line using support vector machine. Int. J. Electr. Power Energy Syst. 32(6), 629–636 (2010). https://doi.org/10.1016/j.ijepes.2009.11.020

    Article  Google Scholar 

  25. N. Shahid, S.A. Aleem, I.H. Naqvi, N. Zaffar, Support vector machine based fault detection & classification in smart grids. Ll, 1526–1531 (2012)

    Google Scholar 

  26. B. Das, J.V. Reddy, Fuzzy-logic-based fault classification scheme for digital distance protection. 20(2), 609–616 (2005)

    Google Scholar 

  27. S.R. Samantaray, P.K. Dash, Pattern recognition based digital relaying for advanced series compensated line. 30, 102–112 (2008). https://doi.org/10.1016/j.ijepes.2007.06.018

  28. R.K. Aggarwal, Q.Y. Xuan, R.W. Dunn, A.T. Johns, A. Bennett, A novel fault classification technique for double-circuit lines based on a combined unsupervised/supervised neural network. IEEE Power Eng. Rev. 19(1), 57–58 (1999). https://doi.org/10.1109/pesw.1999.747303

    Article  Google Scholar 

  29. O.A.S. Youssef, Combined fuzzy-logic wavelet-based fault classification technique for power system relaying. IEEE Trans. Power Deliv. 19(2), 582–589 (2004). https://doi.org/10.1109/TPWRD.2004.826386

    Article  Google Scholar 

  30. F. Gao, J.S. Thorp, S. Gao, A. Pal, K.A. Vance, A voltage phasor based fault-classification method for phasor measurement unit only state estimator output. Electr. Power Compon. Syst. 43(1), 22–31 (2015). https://doi.org/10.1080/15325008.2014.956951

    Article  Google Scholar 

  31. H. Jia, An improved traveling-wave-based fault location method with compensating the dispersion effect of traveling wave in wavelet domain. Math. Probl. Eng. 2017 (2017). https://doi.org/10.1155/2017/1019591

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Conflict of interest statement

Authors declare that there is no conflict of interest in the above work.

Author information

Authors and Affiliations

  1. Jawaharlal Darda Institute of Engineering and Technology, Yavatmal, India

    Prajwal R. Kale

  2. Prof Ram Meghe College of Engineering & Management, Badnera, India

    Kiran A. Dongre

  3. Priyadarshini Indira Gandhi College of Engineering, Nagpur, India

    Muneeb Ahmad

Authors
  1. Prajwal R. Kale
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  2. Kiran A. Dongre
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  3. Muneeb Ahmad
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Corresponding author

Correspondence to Prajwal R. Kale .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Govind Ballabh Pant Institute of Engineering & Technology, Pauri Garhwal, Uttarakhand, India

    Krishan Kant Singh Mer

  2. Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, India

    Vijay Bhaskar Semwal

  3. Department of Electronics and Communication, Institute of Technology Gopeshwar, Gopeshwar, India

    Vishwanath Bijalwan

  4. Universidad Internacional de La Rioja, Logroño, Spain

    Rubén González Crespo

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Kale, P.R., Dongre, K.A., Ahmad, M. (2021). Machine Learning Approaches in Power System Protection: A Review. In: Singh Mer, K.K., Semwal, V.B., Bijalwan, V., Crespo, R.G. (eds) Proceedings of Integrated Intelligence Enable Networks and Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6307-6_73

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