Application of Artificial Neural Networks

  • Waldemar Rebizant
  • Janusz Szafran
  • Andrzej Wiszniewski
Part of the Signals and Communication Technology book series (SCT)


The signal processing methods and algorithms described in preceding chapters were expressed in form of explicit equations, transfer functions and/or logic rules, either in crisp or in fuzzy versions. There are, however, specific tasks and power system operation conditions when, especially for the problems that are complex and difficult to express in terms of traditional means, other solutions should be applied. In such situations, both for signal processing and decision making Artificial Neural Networks may constitute a good solution.


Artificial Neural Network Hide Layer Radial Basis Function Network Secondary Current Neuron Activation Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bothe HH (1998) Neuro-Fuzzy-Methoden. Einfuehrung in Theorie und Anwendungen. Springer-Verlag GmbH, Berlin Heidelberg, ISBN: 978-3-540-57966-4CrossRefGoogle Scholar
  2. 2.
    Bunyagul T, Crossley P, Galac P (2001) Overcurrent protection using signals derived from saturated measurement CTs. In: Proceedings of PES summer meeting, vol 1. Vancouver, pp 103–108Google Scholar
  3. 3.
    Chen KW, Glad ST (1991) Estimation of the primary current in a saturated transformer. In: Proceedings of the 30th conference on decision and control, vol 3. Brighton, England, pp 2363–2365Google Scholar
  4. 4.
    Chow TWS, Cho SY (2007) Neural networks and computing. Imperial College Press, LondonGoogle Scholar
  5. 5.
    Dalstain T, Kulicke B (1995) Neural network approach to fault classification for high speed protective relaying. IEEE Trans Power Deliv 10:1002–1011CrossRefGoogle Scholar
  6. 6.
    Dillon TS (Convenor) (1995) Fault diagnosis in electric power systems through AI techniques. Report by TF 38.06.02, Electra 159Google Scholar
  7. 7.
    El-Sharkawi MA (1995) Neural network application to high performance electric drives systems. In: Proceedings of the IEEE IECON international conference, vol 1, pp 44–49Google Scholar
  8. 8.
    EMTP-ATP Manuals (2001), EEUGGoogle Scholar
  9. 9.
    Fuller R (2000) Introduction to neuro-fuzzy systems. Physica-Verlag, Springer, HeidelbergMATHGoogle Scholar
  10. 10.
    Funabashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Networks 2:183–192CrossRefGoogle Scholar
  11. 11.
    Halinka A, Winkler W, Witek B (1995) Fault detection and recognition in generator-transformer units by neural network based adaptive protection. In: Proceedings of the 30th UPEC conference, vol 1. London, UK, pp 82–84Google Scholar
  12. 12.
    Kang YC, Lim UJ, Kang SH (2004) Compensating algorithm for the secondary current for use with measuring type current transformers. In: Proceedings of the international conference on advanced power system automation and protection, Jeju, Korea, pp 3–8Google Scholar
  13. 13.
    Kang YC, Park JK, Kang SH, Johns AT, Aggarwal RK (1996) Development and hardware implementation of a compensating algorithm for the secondary current of current transformers. IEE Proc Electr Power Appl 243:41–49CrossRefGoogle Scholar
  14. 14.
    Kang YC, Park JK, Kang SH, Johns AT, Aggarwal RK (1997) An algorithm for compensating secondary current of current transformer. IEEE Trans Power Deliv 12:116–124CrossRefGoogle Scholar
  15. 15.
    Kang Y, Kang S, Crossley P (2003) An algorithm for detecting CT saturation using the secondary current third-derivative function. In Proceedings of the IEEE Bologna powertech conference, pp 320–326Google Scholar
  16. 16.
    Kasztenny B, Mazereeuw J, Jones K (2001) CT Saturation in industrial applications—analysis and application guidelines. GE Multilin, Canada, Publ. GET-8501Google Scholar
  17. 17.
    Kasztenny B, Rosołowski E, Łukowicz M, Iżykowski J (1997) Current related relaying algorithm immune to saturation of current transformers. In: Proceedings of the developments in power system protection, Conference publication No. 434, pp 365–368Google Scholar
  18. 18.
    Kezunovic M et al (1994) Neural network applications to real-time and off-line fault analysis. In Proceedings of the conference intelligent system application to power systems, Montpelier, France, pp 665–671Google Scholar
  19. 19.
    Kezunovic M et al (1998) Practical intelligent system applications to protection, and substation monitoring and control. In: Proceedings of the CIGRE Session, Paris, France, Paper 34–104Google Scholar
  20. 20.
    Kezunovic M, Fromen CW, Phillips F (1994) Experimental evaluation of EMTP-based current transformer models for protective relay transient study. IEEE Trans Power Deliv 9:405–413CrossRefGoogle Scholar
  21. 21.
    Koglin HJ, Kostyla P, Lobos T, Waclawek Z (1988) Voltage waveforms analysis for arcing faults detection on transmission lines. In: Proceedings of the 11th international conference on power system protection, Bled, Slovenia, pp 147–152Google Scholar
  22. 22.
    Kohonen T (1984) Self-organization and associative memory. Springer, New YorkMATHGoogle Scholar
  23. 23.
    Li F, Li Y, Aggarwal RK (2002) Combined wavelet transform and regression technique for secondary current compensation of current transformer. IEE Proc Gener Transm Distrib 149:497–503CrossRefGoogle Scholar
  24. 24.
    Lu CN, Wu HT, Vemuri S (1992) Neural networks based short term load forecasting. In proceedings of the IEEE power engineering society winter meeting, WM 12-55 PWRSGoogle Scholar
  25. 25.
    Łukowicz M, Rosołowski E (1998) Artificial neural network based dynamic compensation of current transformer errors. In: Proceedings of the 8th international symposium on short circuit currents in power systems, Brussels, Belgium, pp 19–24Google Scholar
  26. 26.
    MATLAB - Neural Network ToolboxGoogle Scholar
  27. 27.
    Michalik M, Lukowicz M, Rebizant W, Lee SJ, Kang SH (2008) New ANN-Based algorithms for detecting HIFs in multigrounded MV networks. IEEE Trans Power Deliv 23:58–66CrossRefGoogle Scholar
  28. 28.
    Neibur D (Convenor) (1995) Artificial neural networks for power systems. Report by TF 38.06.06, Electra 159Google Scholar
  29. 29.
    Rebizant W (2000) ANN based detection of OS conditions in power system. In: Proceedings of the 12th international conference on power system protection PSP2000, Bled, Slovenia, pp 51–56Google Scholar
  30. 30.
    Rebizant W, Bejmert D (2005) Current transformer saturation detection with genetically optimized neural networks. In: Proceedings of the IEEE powertech conference, St. Petersburg, Russia, paper 220Google Scholar
  31. 31.
    Rebizant W, Bejmert D (2007) Current-transformer saturation detection with genetically optimized neural networks. IEEE Trans Power Deliv 22:820–827CrossRefGoogle Scholar
  32. 32.
    Rebizant W, Bejmert D, Schiel L (2007) Transformer differential protection with neural network based inrush stabilization. In: Proceedings of the IEEE powertech conference, Ecole Polytechnique Federale de Lausanne, Switzerland, paper 607Google Scholar
  33. 33.
    Rebizant W, Bejmert D, Staszewski J, Schiel L (2007) CT saturation detection and correction with artificial neural networks. In: Proceedings of the 2nd international conference on advanced power system automation and protection, Jeju, Korea, paper 504Google Scholar
  34. 34.
    Rebizant W, Szafran J, Feser K, Oechsle F (2001) Evolutionary improvement of neural classifiers for generator out-of-step protection. In: Proceedings of the IEEE porto powertech conference, Porto, Portugal, vol 4, paper PRL1-223Google Scholar
  35. 35.
    Rebizant W, Szafran J, Feser K, Oechsle F (2002) Evolutionaere Optimierung neuronaler Klassifikatoren fuer den Generatorschutz. ELEKTRIE, Berlin 56:51–56Google Scholar
  36. 36.
    Rebizant W, Szafran J, Oechsle F (2001) Out-of-step protection with genetically optimized neural networks. In: Proceedings of the 10th international conference on present-day problems of power engineering, vol. 2. Gdansk-Jurata, Poland, pp 39–46Google Scholar
  37. 37.
    Rebizant W, Hayder T, Schiel L (2004) Prediction of CT saturation period for differential relay adaptation purposes. In: Proceedings of the international conference on advanced power system automation and protection, Jeju, Korea, pp 17–22Google Scholar
  38. 38.
    Rumelhart DE, MccLelland (1986) Parallel distributed processing: exploration in the microstructure of cognition. MIT Press, CambridgeGoogle Scholar
  39. 39.
    Saha MM, Iżykowski J, Łukowicz M, Rosołowski E (2001) Application of ANN method for instrument transformer correction in transmission line protection. In: Proceedings of the IEE development in power system protection conference, Publication No. 479, pp 303–306Google Scholar
  40. 40.
    Santoso NI, Tan OT (1990) A neural network based real-time control of capacitors installed on distribution systems. IEEE Trans Power Deliv 5:266–272CrossRefGoogle Scholar
  41. 41.
    Uhrig RE (1991) Potential applications of neural networks to nuclear power plants. In Proc Am Power Conf 53(2):946–951Google Scholar
  42. 42.
    Wong KP, Phuoc TN, Attikiouzel Y (1990) Transient stability assessment for single machine power systems using neural networks. In: Proceedings of the IEEE conference on computer and communication systems, pp 32–36Google Scholar
  43. 43.
    Wu QH, Hog BW, Irwin GW (1992) A neural network regulator for turbo generators. IEEE Trans Neural Networks 3:95–100CrossRefGoogle Scholar
  44. 44.
    Yu DC, Cummins JC, Wang Z, Yoon HJ, Kojovic LA (2001) Correction of current transformer distorted secondary current due to saturation using artificial neural networks. IEEE Trans Power Deliv 16:189–194CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited  2011

Authors and Affiliations

  • Waldemar Rebizant
    • 1
  • Janusz Szafran
    • 1
  • Andrzej Wiszniewski
    • 1
  1. 1.Wroclaw University of TechnologyWroclawPoland

Personalised recommendations