Islanding detection of synchronous distributed generators using data mining complex correlations

Islanding detection of synchronous distributed generators using data mining complex correlations

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This study proposes a novel method based on data mining for islanding detection of synchronous distributed generators. The method uses a new technique called data mining of code repositories (DAMICORE), which is a powerful data mining tool for detecting patterns and similarities in various kinds of datasets. In addition, a trip logic was developed in this proposal in order to detect islanding and disconnect the distributed generator. One of the most relevant features of the proposed method is its capability to generalise, which reduces the need of big datasets for training purposes. This approach has been tested with islanding, load switching and fault simulations, presenting promising results concerning performance, as well as detection time. General results showed a better performance of the method if compared to traditional anti-islanding protection schemes, such as frequency-based relays.


    1. 1)
      • 1. Dugan, R.C., Arritt, R.F., McDermott, T.E., et al: ‘Distribution system analysis to support the smart grid’. IEEE PES General Meeting, Providence, RI, USA, July 2010, pp. 1–8.
    2. 2)
      • 2. Camarinha-Matos, L.M.: ‘Collaborative smart grids – a survey on trends’, Renew. Sust. Energy Rev., 2016, 65, pp. 283294.
    3. 3)
      • 3. Yan, Y., Qian, Y., Sharif, H., et al: ‘A survey on smart grid communication infrastructures: motivations, requirements and challenges’, IEEE Commun. Surv. Tutor., 2013, 15, (1), pp. 520.
    4. 4)
      • 4. Roy, N.K., Pota, H.R.: ‘Current status and issues of concern for the integration of distributed generation into electricity networks’, IEEE Syst. J., 2015, 9, pp. 933944.
    5. 5)
      • 5. IEEE: ‘IEEE standard for interconnecting distributed resources with electric power systems’, IEEE Std 1547-2003, July 2003, pp. 128.
    6. 6)
      • 6. Walling, R., Miller, N.: ‘Distributed generation islanding-implications on power system dynamic performance’. 2002 IEEE Power Engineering Society Summer Meeting, vol. 1, July 2002, pp. 9296.
    7. 7)
      • 7. Jenkins, N., Ekanayake, J.B., Strbac, G.: ‘Distributed generation’ (Institution of Engineering and Technology, London, UK, 2010).
    8. 8)
      • 8. Mahat, P., Chen, Z., Bak-Jensen, B.: ‘Review of islanding detection methods for distributed generation’. Third Int. Conf. Electric Utility Deregulation and Restructuring and Power Technologies (DRPT 2008), April 2008, pp. 27432748.
    9. 9)
      • 9. Kar, S., Samantaray, S.: ‘Data-mining-based intelligent anti-islanding protection relay for distributed generations’, IET Gener., Transm. Distrib., 2014, 8, pp. 629639.
    10. 10)
      • 10. Far, H., Rodolakis, A., Joos, G.: ‘Synchronous distributed generation islanding protection using intelligent relays’, IEEE Trans. Smart Grid, 2012, 3, pp. 16951703.
    11. 11)
      • 11. Dash, P., Barik, S., Patnaik, R.: ‘Detection and classification of islanding and nonislanding events in distributed generation based on fuzzy decision tree’, J. Control, Autom. Electr. Syst., 2014, 25, (6), pp. 699719.
    12. 12)
      • 12. Li, S., Rodolakis, A.J., El-Arroudi, K., et al: ‘Islanding protection of multiple distributed resources under adverse islanding conditions’, IET Gener., Transm. Distrib., 2016, 10, (8), pp. 19011912.
    13. 13)
      • 13. Kantardzic, M.: ‘Data mining: concepts, models, methods, and algorithms’ (John Wiley & Sons, Hoboken, NJ, USA, 2011, 2nd edn.).
    14. 14)
      • 14. Breiman, L.: ‘Random forests’, Mach. Learn., 2001, 45, (1), pp. 532.
    15. 15)
      • 15. Merlin, V., Santos, R., Grilo, A., et al: ‘A new artificial neural network based method for islanding detection of distributed generators’, Int. J. Electr. Power Energy Syst., 2016, 75, pp. 139151.
    16. 16)
      • 16. Raza, S., Mokhlis, H., Arof, H., et al: ‘Minimum-features-based ann-pso approach for islanding detection in distribution system’, IET Renew. Power Gener., 2016, 10, (9), pp. 12551263.
    17. 17)
      • 17. Ray, P.K., Mohanty, S.R., Kishor, N.: ‘Disturbance detection in grid-connected distributed generation system using wavelet and s-transform’, Electr. Power Syst. Res., 2011, 81, pp. 805819.
    18. 18)
      • 18. Alshareef, S., Talwar, S., Morsi, W.G.: ‘A new approach based on wavelet design and machine learning for islanding detection of distributed generation’, IEEE Trans. Smart Grid, 2014, 5, pp. 15751583.
    19. 19)
      • 19. Mohanty, S.R., Kishor, N., Ray, P.K., et al: ‘Comparative study of advanced signal processing techniques for islanding detection in a hybrid distributed generation system’, IEEE Trans. Sustain. Energy, 2015, 6, pp. 122131.
    20. 20)
      • 20. Matic-Cuka, B., Kezunovic, M.: ‘Islanding detection for inverter-based distributed generation using support vector machine method’, IEEE Trans. Smart Grid, 2014, 5, pp. 26762686.
    21. 21)
      • 21. de Abreu Silva, B., Cuminato, L.A., Delbem, A.C., et al: ‘Application-oriented cache memory configuration for energy efficiency in multicores’, IET Comput. Digit. Tech., 2014, 9, (1), pp. 7381.
    22. 22)
      • 22. Martins, L.G.A., Nobre, R., Cardoso, J.M.P., et al: ‘Clustering-based selection for the exploration of compiler optimization sequences’, ACM Trans. Architec. Code Optim., 2015, 1, p. 22.
    23. 23)
      • 23. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: ‘The KDD process for extracting useful knowledge from volumes of data’, Commun. ACM, 1996, 39, (11), pp. 2734.
    24. 24)
      • 24. Saitou, N., Nei, M.: ‘The neighbor-joining method: a new method for reconstructing phylogenetic trees’, Mol. Biol. Evol., 1987, 4, (4), pp. 406425.
    25. 25)
      • 25. Newman, M.E.: ‘Fast algorithm for detecting community structure in networks’, Physical Review E, 2004, 69, (6), p. 066133.
    26. 26)
      • 26. IEEE recommended practice for excitation system models for power system stability studies’, IEEE Std 421.5-1992, 1992.
    27. 27)
      • 27. Report, I.: ‘Dynamic models for steam and hydro turbines in power system studies’, IEEE Trans. Power Appar. Syst., 1973, PAS-92, pp. 19041915.

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