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Fault detection in smart grids with time-varying distributed generation using wavelet energy and evolving neural networks

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Online monitoring systems have been developed for real-time detection of high impedance faults in power distribution networks. Sources of distributed generation are usually ignored in the analyses. Distributed generation imposes great challenges to monitoring systems. This paper proposes a wavelet transform-based feature-extraction method combined with evolving neural networks to detect and locate high impedance faults in time-varying distributed generation systems. Empirically validated IEEE models, simulated in the ATPDraw and Matlab environments, were used to generate data streams containing faulty and normal occurrences. The energy of detail coefficients obtained from different wavelet families such as Symlet, Daubechies, and Biorthogonal are evaluated as feature extraction method. The proposed evolving neural network approach is particularly supplied with a recursive algorithm for learning from online data stream. Online learning allows the neural models to capture novelties and, therefore, deal with nonstationary behavior. This is a unique characteristic of this type of neural network, which differentiate it from other types of neural models. Comparative results considering feed-forward, radial-basis, and recurrent neural networks as well as the proposed hybrid wavelet-evolving neural network approach are shown. The proposed approach has provided encouraging results in terms of accuracy and robustness to changing environment using the energy of detail coefficients of a Symlet-2 wavelet. Robustness to the effect of distributed generation and to transient events is achieved through the ability of the neural model to update parameters, number of hidden neurons, and connection weights recursively. New conditions could be captured on the fly, during the online operation of the system.

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  1. Andonovski G, Music G, Blazic S, Skrjanc I (2018) Evolving model identification for process monitoring and prediction of non-linear systems. Eng Appl Artif Intell 68:214–221

  2. Angelov P, Gu X (2018) Deep rule-based classifier with human-level performance and characteristics. Inf Sci 463:196–213

  3. Baqui I, Zamora I, Mazn J, Buigues G (2011) High impedance fault detection methodology using wavelet transform and artificial neural networks. Electr Power Syst Res 81:1325–1333

  4. Bezerra C, Costa B, Guedes LA, Angelov P (2016) An evolving approach to unsupervised and real-time fault detection in industrial processes. Expert Syst Appl 63:134–144

  5. Bretas AS, Moreto M, Salim RH, Pires LO (2006) A novel high impedance fault location for distribution systems considering distributed generation. In: IEEE/PES transmission and distribution conference, pp 433–439

  6. Bretas AS, Pires L, Moreto M, Salim RH, Huang D, Li K, Irwing G (2006) A bp neural network based technique for hif detection and location on distribution systems with distributed generation. Int Conf Intell Comput 4114:608–613

  7. Cha SH (2007) Comprehensive survey on distance/similarity measures between probability density fuctions. Int J Math Models Methods Appl Sci 1(4):300–307

  8. Costa FB, Souza BA, Brito NS, Silva JAC (2015) Real-time detection of transients induced by high impedance faults based on the boundary wavelet transform. IEEE Trans Ind Appl 51(6):5312–5323

  9. Emanuel A, Cyganski D, Orr J, Shiller S, Gulachenski E (1990) High impedance fault arcing on sandy soil in 15 kv distribution feeders: contributons to the evaluation of the low frequency spectrum. IEEE Trans Power Deliv 5(2):676–686

  10. Etemadi AH, Sanaye-Pasand M (2008) High-impedance fault detection using multi-resolution signal decomposition and adaptive neural fuzzy inference system. IET Gener Transm Distrib 2(1):110–118

  11. Ezzt M, Marei MI, Abdel-Rahman M, Mansour MM (2007) A hybrid strategy for distributed generators islanding detection. In: IEEE Power Engineering Society conference & expo, pp 1–7

  12. Garcia C, Leite D, Skrjanc I (2019) Incremental missing-data imputation for evolving fuzzy granular prediction. IEEE Trans Fuzzy Syst 1–15.

  13. Garcia-Santander L, Bastard P, Petit M, Gal I, Lopez E, Opazo H (2005) Down-conductor fault detection and via a voltage based method for radial distribution networks. IEEE Proc Gener Transm Distrib 152(2):180–184

  14. Ghaderi A, Ginn HL, Mohammadpour HA (2017) High impedance fault detection: a review. Electr Power Syst Res 143:376–388

  15. Ghaderi A, Mohammadpour HA, Ginn H (2015) High impedance fault detection method efficiency: simulation vs real-world data acquisition. In: IEEE power & energy conference, pp 1–5

  16. Gomez JC, Vaschetti J, Coyos C, Ibarlucea C (2013) Distributed generation: impact on protections and power quality. IEEE Latin Am Trans 11(1):460–465

  17. Haykin S (2008) Neural networks and learning machines, 3rd edn. Pearson, London

  18. Hyde R, Angelov P, Mackenzie A (2017) Fully online clustering of evolving data streams into arbitrarily shaped clusters. Info Sci 382:96–114

  19. Ibrahim DK, Eldin E, Aboul-Zahab E, Saleh S (2010) Real time evaluation of DWT-based high impedance fault detection in EHV transmission. Electr Power Syst Res 80:907–914

  20. Kasabov N (2007) Evolving connectionist systems: the knowledge engineering approach. Springer, Secaucus

  21. Kim CH, Aggarwal R (2000) Wavelet transforms in power systems. Power Eng J 14(2):81–87

  22. Leite D, Andonovski G, Škrjanc I, Gomide F (2019) Optimal rule-based granular systems from data streams. IEEE Trans Fuzzy Syst 1–14.

  23. Leite D, Ballini R, Costa P, Gomide F (2012) Evolving fuzzy granular modeling from nonstationary fuzzy data streams. Evol Syst 3(2):65–79

  24. Leite D, Costa P, Gomide F (2013) Evolving granular neural networks from fuzzy data streams. Neural Netw 38:1–16

  25. Leite D, Palhares R, Campos V, Gomide F (2015) Evolving granular fuzzy model-based control of nonlinear dynamic systems. IEEE Trans Fuzzy Syst 23(4):923–938

  26. Lucas F, Costa P, Batalha R, Leite D (2018) High impedance fault detection in time-varying distributed generation systems using adaptive neural networks. In: International joint conference on neural networks (IJCNN), pp 1–8

  27. Lughofer E, Pratama M, Skrjanc I (2017) Incremental rule splitting in generalized evolving fuzzy systems for autonomous drift compensation. IEEE Trans Fuzzy Syst PP(99):1-1

  28. Mohamad S, Sayed-Mouchaweh M, Bouchachia A (2018) Active learning for classifying data streams with unknown number of classes. Neural Netw 98:1–15

  29. Mora-Florez J, Melendez J, Carrillo-Caicedo G (2008) Comparison of impedance based fault location methods for power distribution systems. Electr Power Syst Res 78(4):657–666

  30. Pratama M, Anavatti SG, Angelov P, Lughofer E (2014) Panfis: a novel incremental learning machine. IEEE Trans Neural Netw Learn Syst 25(1):55–68

  31. Pratama M, Lughofer E, Lim C, Rahayu W, Dillon T, Budiyono A (2017) A novel evolving semi-supervised classifier. Int J Fuzzy Syst 19:863–880

  32. Rubio JJ (2009) Sofmls: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309

  33. Rubio JJ (2014) Evolving intelligent algorithms for the modeling of brain and eye signals. Appl Soft Comput 14(B):259–268

  34. Santos WC, Lopes FV, Brito NSD, Souza B (2017) High impedance fault identification on distribution networks. IEEE Trans Power Deliv 32(1):23–32

  35. Sedighi AR, Haghifam MR, Malik OP (2005) Soft computing applications in high impedance fault detection in distribution systems. Electr Power Syst Res 76:136–144

  36. Sedighizadeh M, Rezazadeh A, Elkalashy NI (2010) Approaches in high impedance fault detection: a chronological review. Adv Electr Comput Eng 10(3):114–128

  37. Silva JA, Neves WL, Costa FB, Souza BA, Santos WC (2013) High impedance fault location; case study using wavelet transform and artificial neural networks. In: International conference on electricity distribution, pp 1–4

  38. Silva S, Costa P, Gouvea M, Lacerda A, Alves F, Leite D (2018) High impedance fault detection in power distribution systems using wavelet transform and evolving neural network. Electr Power Syst Res 154:474–483

  39. Škrjanc I, Iglesias JA, Sanchis A, Leite D, Lughofer E, Gomide F (2019) Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: a survey. Inf Sci 490:344–368

  40. Torres V, Guardado JL, Ruiz HF, Maximov S (2014) Modeling and detection of high impedance faults. Int J Electr Power 61(1):163–172

  41. Wai DC, Yibin X (1998) A novel technique for high impedance fault identification. IEEE Trans Power Deliv 13(3):738–744

  42. Watts JM (2009) A decade of evolving connectionist systems: a review. IEEE Trans Syst Man Cybern 39:253–269

  43. Xiangjum Z, Li KK, Chan WL, Sheng S (2004) Multi-agents based protection for distributed generation systems. In: IEEE international conference on electric utility deregulation, restructuring and power technology, pp 393–397

  44. Yee P, Haykin S (2001) Regularized radial basis function networks: theory and applications. Wiley, New York

  45. Zamanan N, Sykulski J (2014) The evolution of high impedance fault modeling. In: IEEE international conference on harmonics and quality of power, pp 77–81

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Correspondence to Daniel Leite.

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This study was funded by Instituto Serrapilheira (Grant no. Serra-1812-26777) and Javna Agencija za Raziskovalno Dejavnost RS (Grant no. P2-0219).

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Lucas, F., Costa, P., Batalha, R. et al. Fault detection in smart grids with time-varying distributed generation using wavelet energy and evolving neural networks. Evolving Systems (2020).

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  • Evolving neural network
  • Fault detection
  • Smart grid
  • Distributed generation
  • Wavelet transform