Harmonic content extraction in converter waveforms using radial basis function neural networks (RBFNN) and p-q power theory

In this paper radial basis function neural network (RBFNN) is used to extract total harmonics in converter waveforms. The methodology is based on p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the harmonics over a wide operating range are extracted. The proposed RBFNN filtering training algorithms are based on an efficient training method called hybrid learning method - computation is systematic. The method requires small size network, very robust, and the proposed algorithms are very effective. The analysis is verified using MATLAB/SIMULINK simulation.