Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Radial Basis Function Neural Networks (RBFNN) and p-q Power Theory Based Harmonic Identification in Converter Waveforms

  • Almaita, Eyad K. (Dept. of Electrical and Computer Eng., Western Michigan University) ;
  • Asumadu, Johnson A. (Dept. of Electrical and Computer Eng., Western Michigan University)
  • Received : 2010.11.05
  • Published : 2011.11.20
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Abstract

In this paper, two radial basis function neural networks (RBFNNs) are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the types of harmonic content are identified over a wide operating range. Constant power and sinusoidal current compensation strategies are investigated in this paper. The RBFNN filtering training algorithm is based on a systematic and computationally efficient training method called the hybrid learning method. In this new methodology, the RBFNN is combined with the p-q theory to extract the harmonics content in converter waveforms. The small size and the robustness of the resulting network models reflect the effectiveness of the algorithm. The analysis is verified using MATLAB simulations.

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