Radial Basis Function Neural Networks (RBFNN) and p-q Power Theory Based Harmonic Identification in Converter Waveforms


Vol. 11, No. 6, pp. 922-930, Nov. 2011
10.6113/JPE.2011.11.6.922


PDF    

 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.


 Statistics
Show / Hide Statistics

Cumulative Counts from September 30th, 2019
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.



Cite this article

[IEEE Style]

E. K. Almaita and J. A. Asumadu, "Radial Basis Function Neural Networks (RBFNN) and p-q Power Theory Based Harmonic Identification in Converter Waveforms," Journal of Power Electronics, vol. 11, no. 6, pp. 922-930, 2011. DOI: 10.6113/JPE.2011.11.6.922.

[ACM Style]

Eyad K. Almaita and Johnson A. Asumadu. 2011. Radial Basis Function Neural Networks (RBFNN) and p-q Power Theory Based Harmonic Identification in Converter Waveforms. Journal of Power Electronics, 11, 6, (2011), 922-930. DOI: 10.6113/JPE.2011.11.6.922.