A Precise Electrical Disturbance Generator for Neural Network Training with Real Level Output

  • Antonio García
  • Carlos León
  • Iñigo Monedero
  • Jorge Ropero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


Power Quality is defined as the study of the quality of electric power lines. The detection and classification of the different disturbances which cause power quality problems is a difficult task which requires a high level of engineering expertise. Thus, neural networks are usually a good choice for the detection and classification of these disturbances. This paper describes a powerful tool, developed by the Institute for Natural Resources and Agrobiology at the Scientific Research Council (CSIC) and the Electronic Technology Department at the University of Seville, which generates electrical patterns of disturbances for the training of neural networks for PQ tasks. This system has been expanded to other applications (as comparative test between PQ meters, or test of effects of power-line disturbances on equipment) through the addition of a specifically developed high fidelity power amplifier, which allows the generation of disturbed signals at real levels.


Power quality electrical disturbance neural network 


  1. 1.
    McGranaghan, M., Roettger, B.: Economic Evaluation of Power Quality. 0272-1724/02 (2002)Google Scholar
  2. 2.
    Hussain, A., Sukairi, M.H., Mohamed, A., Mohamed, R.: Automatic Detection Of Power Quality Disturbances and Identification of Transients Signals. In: International Symposium on Signal Processing and its Applications, Kuala Lumpur, Malaysia, August 13-16, 2001 (2001)Google Scholar
  3. 3.
    Adapa, R.: Power Quality Analysis Software, 0272-1724/02 IEEE (2002)Google Scholar
  4. 4.
    Anis Ibrahim, W.R., Morcos, M.M.: Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey. 668 IEEE Transactions on Power Delivery 17 (2002)Google Scholar
  5. 5.
    Zheng, G., Shi, M.X., Liu, D., Yao, J., Mao, Z.M.: Power Quality Disturbance Classification Based on Rule-Based and Wavelet-Multi-Resolution Decomposition. In: Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, November 4-5 (2002)Google Scholar
  6. 6.
    Lai, L.L.: Wavelet-based Neural Network for Power Quality Recognition. 0-7803-7322-7, IEEE (2002)Google Scholar
  7. 7.
    Xiangxun, C.: Wavelet-based Measurement and Classification of Power Quality Disturbances, 0-7803-7242-05/02, IEEE (2002)Google Scholar
  8. 8.
    Borrás, D., Castilla, M., Moreno, N., Montaño, J.C.: Wavelet and neural structure: a new tool for diagnostic of power system disturbances. IEEE Trans. on Industry Applications 37(1), 184–190 (2001)CrossRefGoogle Scholar
  9. 9.
    Bhargava, B.: Arc Furnace Flicker Measurements and Control. IEEE Transactions on Power Delivery 8(1), 409–423 (1993)CrossRefGoogle Scholar
  10. 10.
    Topalis, F.V., Gonos, I.F., Vokas, G.A.: Arbitrary wavwform generator for harmonic distortion test on compact fluorescent lamps. Measurement 30, 257–267 (2001)CrossRefGoogle Scholar
  11. 11.
    Anderson, L.M., Bowes, K.B.: The effects of Power-line disturbances on consumer electronic equipment. IEEE Transactions on Power Delivery 5(2), 1062–1065 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Antonio García
    • 1
  • Carlos León
    • 1
  • Iñigo Monedero
    • 1
  • Jorge Ropero
    • 1
  1. 1.Escuela Técnica Superior de Ingeniería Informática, Departamento de Tecnología Electrónica, Avda, Reina Mercedes s/n, 41012 SevillaSpain

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