access icon free Adaptive protection combined with machine learning for microgrids

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.

Inspec keywords: power engineering computing; support vector machines; neural nets; smart power grids; learning (artificial intelligence); data mining; distributed power generation

Other keywords: hybrid artificial neural network; microgrid model; uncertain elements; rule-based adaptive protection scheme; support vector machine; adaptive reconfigurations; protective settings; machine-learning methodology; data mining; machine learning; growing massive data streams; Pearson correlation coefficients; extensive distribution automation; state recognition

Subjects: Data handling techniques; Neural computing techniques; Optimisation techniques; Knowledge engineering techniques; Distributed power generation; Power engineering computing; Other topics in statistics

References

    1. 1)
      • 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.
    2. 2)
      • 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.
    3. 3)
      • 22. Cannas, B., Celli, G., Marchesi, M., et al: ‘Neural networks for power system condition monitoring and protection’, Neuro Comput., 1998, 23, pp. 111123.
    4. 4)
      • 1. Lasseter, R.H.: ‘Microgrids’. Power Engineering Society Winter Meeting, New York, NY, USA, 2002, vol. 1.
    5. 5)
      • 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.
    6. 6)
      • 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.
    7. 7)
      • 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.
    8. 8)
      • 15. Oudalov, A., Fidigatti, A.: ‘Adaptive network protection in microgrids’, Int. J. Distrib. Energy Res., 2009, pp. 201226.
    9. 9)
      • 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.
    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)
      • 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.
    12. 12)
      • 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.
    13. 13)
      • 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.
    14. 14)
      • 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.
    15. 15)
      • 26. Benesty, J., Chen, J., Huang, Y.: ‘Pearson correlation coefficient’, ‘Noise reduction in speech processing’ (Springer, Berlin, Heidelberg, 2009), pp. 14.
    16. 16)
      • 2. Hengwei, L., Chengxi, L., Guerrero, J.M., et al: ‘Modular power architectures for microgrid clusters’. Green Energy, Sfax, Tunisia, 2014, pp. 199206.
    17. 17)
      • 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.
    18. 18)
      • 5. Blackburn, J.L.: ‘Protective relaying principles and applications’ (Marcel Dekker, New York, 1998), pp. 383408.
    19. 19)
      • 23. Tan, Z.-H.: ‘Hybrid evolutionary approach for designing neural networks for classification’, IEE Electron. Lett., 2004, 40, (15), pp. 955957.
    20. 20)
      • 6. Jayawarna, N., Jenkins, N., Barnes, M., et al: ‘Safety analysis of a microgrid’. Future Power Systems, Amsterdam, Netherlands, 2005.
    21. 21)
      • 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.
    22. 22)
      • 4. Anthony, M.A.: ‘Electric power system protection and coordination’ (McGraw-Hill, New York, 1995), pp. 342346.
    23. 23)
      • 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.
    24. 24)
      • 3. Anderson, P.M.: ‘Power system protection’ (Wiley, New York, 1999), pp. 201240.
    25. 25)
      • 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.
    26. 26)
      • 14. Kar, S., Samantaray, S.R.: ‘Time-frequency transform-based differential scheme for microgrid protection’, IET Gener. Transm. Distrib., 2014, 8, pp. 310320.
    27. 27)
      • 25. Bishop, C.: ‘Pattern recognition and machine learning’ (Springer, UK, 2006), pp. 232240.
    28. 28)
      • 17. Haughton, D.A., Heydt, G.: ‘A linear state estimation formulation for smart distribution systems’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 11871195.
    29. 29)
      • 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.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.6230
Loading

Related content

content/journals/10.1049/iet-gtd.2018.6230
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading