Adaptive protection combined with machine learning for microgrids

Adaptive protection combined with machine learning for microgrids

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This paper presents a rule-based adaptive protection scheme using machine-learning methodology for microgrids in extensive distribution automation (DA). The uncertain elements in a microgrid are first analysed quantitatively by Pearson correlation coefficients from data mining. Then, a so-called hybrid artificial neural network and support vector machine (ANN-SVM) model is proposed for state recognition in microgrids, which utilises the growing massive data streams in smart grids. Based on the state recognition in the algorithm, adaptive reconfigurations can be implemented with enhanced decision-making to modify the protective settings and the network topology to ensure the reliability of the intelligent operation. The effectiveness of the proposed methods is demonstrated on a microgrid model in Aalborg, Denmark and an IEEE 9 bus model, respectively.


    1. 1)
      • 1. Lasseter, R.H.: ‘Microgrids’. Power Engineering Society Winter Meeting, New York, NY, USA, 2002, vol. 1.
    2. 2)
      • 2. Hengwei, L., Chengxi, L., Guerrero, J.M., et al: ‘Modular power architectures for microgrid clusters’. Green Energy, Sfax, Tunisia, 2014, pp. 199206.
    3. 3)
      • 3. Anderson, P.M.: ‘Power system protection’ (Wiley, New York, 1999), pp. 201240.
    4. 4)
      • 4. Anthony, M.A.: ‘Electric power system protection and coordination’ (McGraw-Hill, New York, 1995), pp. 342346.
    5. 5)
      • 5. Blackburn, J.L.: ‘Protective relaying principles and applications’ (Marcel Dekker, New York, 1998), pp. 383408.
    6. 6)
      • 6. Jayawarna, N., Jenkins, N., Barnes, M., et al: ‘Safety analysis of a microgrid’. Future Power Systems, Amsterdam, Netherlands, 2005.
    7. 7)
      • 7. Hengwei, L., Chengxi, L., Guerrero, J.M., et al: ‘Distance protection for microgrids in distribution system’. 41st Annual Conf. of the IEEE Industrial Electronics Society (IECON2015), Japan, Yokohama, November 2015, pp. 731736.
    8. 8)
      • 8. Dewadasa, M.: ‘Protection for distributed generation interfaced networks’. PhD thesis, Dept. Electrical Engineering, Faculty of Built Environment and Engineering, Queensland University of Technology, Australia, 2010.
    9. 9)
      • 9. Dewadasa, M., Majumder, R., Ghosh, A., et al: ‘Control and protection of a microgrid with converter interfaced micro sources’. Third Int. Conf. on Power Systems, Kharagpur, India, 2009, pp. 16.
    10. 10)
      • 10. Nikkhajoei, H., Lasseter, R.H.: ‘Microgrid fault protection based on symmetrical and differential current components’. Power System Engineering Research Center, 2006.
    11. 11)
      • 11. Rockefeller, G.D., Wagner, C.L., Linders, J.R., et al: ‘Adaptive transmission relaying concepts for improved performance’, IEEE Trans. Power Deliv., 1988, 3, pp. 14461458.
    12. 12)
      • 12. Brahma, S.M., Girgis, A.A.: ‘Development of adaptive protection scheme for distribution systems with high penetration of distributed generation’, IEEE Trans. Power Deliv., 2004, 19, pp. 5663.
    13. 13)
      • 13. Dhar, S., Dash, P.K.: ‘Differential current-based fault protection with adaptive threshold for multiple PV-based DC microgrid’, IET Renew. Power Gener., 2017, 11, pp. 778790.
    14. 14)
      • 14. Kar, S., Samantaray, S.R.: ‘Time-frequency transform-based differential scheme for microgrid protection’, IET Gener. Transm. Distrib., 2014, 8, pp. 310320.
    15. 15)
      • 15. Oudalov, A., Fidigatti, A.: ‘Adaptive network protection in microgrids’, Int. J. Distrib. Energy Res., 2009, pp. 201226.
    16. 16)
      • 16. Mahat, P., Zhe, C., Bak-Jensen, B., et al: ‘A simple adaptive overcurrent protection of distribution systems with distributed generation’, IEEE Trans. Smart Grid, 2011, 2, pp. 428437.
    17. 17)
      • 17. Haughton, D.A., Heydt, G.: ‘A linear state estimation formulation for smart distribution systems’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 11871195.
    18. 18)
      • 18. Mirsaeidi, S., Said, D.M.: ‘A protection strategy for micro-grids based on positive-sequence component’, IET Renew. Power Gener., 2015, 9, pp. 600609.
    19. 19)
      • 19. Hengwei, L., Guerrero, J.M., Chenxi, J.: ‘Adaptive overcurrent protection for microgrids in extensive distribution systems’. 42nd Annual Conf. of the IEEE Industrial Electronics Society (IECON2016), Italy, Florence, October 2016, pp. 40424047.
    20. 20)
      • 20. Joe-Air, J., Jun-Zhe, Y., Ying-Hong, L., et al: ‘An adaptive PMU based fault detection/location technique for transmission lines. I. Theory and algorithms’, IEEE Trans. Power Deliv., 2000, 15, pp. 486493.
    21. 21)
      • 21. Joe-Air, J., Ying-Hong, L., Jun-Zhe, Y., et al: ‘An adaptive PMU based fault detection/location technique for transmission lines. II. PMU implementation and performance evaluation’, IEEE Trans. Power Deliv., 2000, 15, pp. 11361146.
    22. 22)
      • 22. Cannas, B., Celli, G., Marchesi, M., et al: ‘Neural networks for power system condition monitoring and protection’, Neuro Comput., 1998, 23, pp. 111123.
    23. 23)
      • 23. Tan, Z.-H.: ‘Hybrid evolutionary approach for designing neural networks for classification’, IEE Electron. Lett., 2004, 40, (15), pp. 955957.
    24. 24)
      • 24. Saha, M.M., Izykowski, J.J., Rosolowski, E.: ‘Fault location on power networks’ (Springer Science & Business Media, Springer-Verlag, London, UK, 2009), pp. 395399.
    25. 25)
      • 25. Bishop, C.: ‘Pattern recognition and machine learning’ (Springer, UK, 2006), pp. 232240.
    26. 26)
      • 26. Benesty, J., Chen, J., Huang, Y.: ‘Pearson correlation coefficient’, ‘Noise reduction in speech processing’ (Springer, Berlin, Heidelberg, 2009), pp. 14.
    27. 27)
      • 27. Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: ‘An on-line relay coordination algorithm for adaptive protection using linear programming technique’, IEEE Trans. Power Deliv., 1996, 11, pp. 165173.
    28. 28)
      • 28. Hengwei, L., Guerrero, J.M., Vasquez, J.C., et al: ‘Adaptive distance protection for microgrids’. 41st Annual Conf. of the IEEE Industrial Electronics Society (IECON2015), Japan, Yokohama, November 2015, pp. 725730.
    29. 29)
      • 29. Salat, R., Osowski, S.: ‘Accurate fault location in the power transmission line using support vector machine approach’, IEEE Trans. Power Syst., 2004, 19, pp. 979986.

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