Self Organizing Map (SOM) approach for classification of Power Quality events

GERMEN E., Gökhan Ece D., Gerek O. N.

15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005, Warszawa, Poland, 11 - 15 September 2005, vol.3696 LNCS, pp.403-408 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 3696 LNCS
  • Doi Number: 10.1007/11550822_63
  • City: Warszawa
  • Country: Poland
  • Page Numbers: pp.403-408
  • Anadolu University Affiliated: Yes


In this work, Self Organizing Map (SOM) is used in order to classify the types of defections in electrical systems, known as Power Quality (PQ) events. The features for classifications are extracted from real time voltage waveform within a sliding time window and a signature vector is formed. The signature vector consists of different types of features such as local wavelet transform extrema at various decomposition levels, spectral harmonic ratios and local extrema of higher order statistical parameters. Before the classification, the clustering has been achieved using SOM in order to define codebook vectors, then LVQ3 (Learning Vector Quantizer) algorithm is applied to find exact classification borders. The k-means algorithm with Davies-Boulding clustering index method is applied to figure out the classification regions. Here it has been observed that, successful classification of two major PQ event types corresponding to arcing faults and motor start-up events for different load conditions has been achieved. © Springer-Verlag Berlin Heidelberg 2005.