Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Complex Neural Classifiers for Power Quality Data Mining

  • Vidhya, S. (Dept. of Computer Science and Engineering, Sri Lakshmi Ammal Engineering College) ;
  • Kamaraj, V. (Dept. of Electrical and Electronics Engineering, SSN College of Engineering)
  • Received : 2017.07.05
  • Accepted : 2018.01.30
  • Published : 2018.07.01
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Abstract

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

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References

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