Facilitating On-Line Harmonic Estimation Based on Robust Adaptive RBFNN

In this paper, An adaptiveRadial Basis Function Neural Networks (RBFNN) algorithm is used to estimate the fundamental and harmonic components of nonlinear load current. The learning rates for adaptive RBFNN are further investigated to minimize the total error and to minimize the error in each of the fundamental and harmonics components. The performance of the adaptive RBFNN is evaluated based on the difference between the original signal and the constructed signal (the summation between fundamental and harmonic components). The methodology used in this paper facilitates the development and design of signal processing and control systems. This is done by training the system and obtaining the initial parameters for the RBFNN based on simulation. After that, the adaptive RBFNN can be in the real system with these initial parameters.