Pattern recognition applications for power system disturbance classification

A. M. Gaouda, S. H. Kanoun, M. M.A. Salama, A. Y. Chikhani

Research output: Contribution to journalArticlepeer-review

104 Citations (Scopus)


This paper presents an automated online disturbance classification technique. This technique is based on wavelet multiresolution analysis and pattern recognition techniques. The wavelet-multiresolution transform is introduced as a powerful tool for feature extraction in order to classify different disturbances. Minimum Euclidean distance, k-nearest neighbor, and neural network classifiers are used to evaluate the efficiency of the extracted features.

Original languageEnglish
Pages (from-to)677-683
Number of pages7
JournalIEEE Transactions on Power Delivery
Issue number3
Publication statusPublished - Jul 2002
Externally publishedYes


  • K-nearest neighbor
  • Minimum Euclidean distance
  • Muitiresolution signal decomposition
  • Neural network recognition techniques
  • Power quality
  • Wavelet analysis

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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