Shunt Active Filter Based on Radial Basis Function Neural Network and p-q Power Theory - Prakash Ch. Tah; Anup K. Panda; Bibhu P. Panigrahi (Institute of Advanced Engineering and Science, 2017)
In this paper a new combination Radial Basis Function Neural Network and p-q Power Theory (RBFNN-PQ) proposed to control shunt active power filters (SAPF). The recommended system has better specifications in comparison with other control methods. In the proposed combination an RBF neural network is employed to extract compensation reference current when supply voltages are distorted and/or unbalance sinusoidal. In order to make the employed model much simpler and tighter an adaptive algorithm for RBF network is proposed. The proposed RBFNN filtering algorithm is based on efficient training methods called hybrid learning method.The method requires a small size network, very robust, and the proposed algorithms are very effective. Extensive simulations are carried out with PI as well as RBFNN controller for p-q control strategies by considering different voltage conditions and adequate results were presented.