An Adaptive Real-Time Technique for Harmonics Estimation Using Adaptive Radial Basis Function Neural Network

In this paper, a neural networks algorithm based on adaptive radial basis function (ARBF) is used to decompose the grid current drawn by nonlinear load, and the fundamental and harmonic components are estimated. The learning rate – considered as one of the most important parameters that govern the performance of the ARBF network - is investigated as well to reduce the system total error. Two methodologies are proposed to improve the estimation of the fundamental component of highly nonlinear current signal. One is based on fast Fourier transform (FFT) and the other is based on least mean square error (LMSE). The error between the reference signal and the reproduced signal (the sum of estimated fundamental and harmonic signals) is chosen as performance index. The obtained results unveil that both methodologies can be effective in enhancing the system accuracy, and that the proposed algorithm can provide better performance compared to the conventional RBF network.