IMPROVING THE ACTIVE POWER FILTER PERFORMANCE WITH AN ADAPTIVE LINEAR NEURAL NETWORK BASED ON THE REFERENCE GENERATION

Abstract

The use of artificial neural networks (ANNs) in power system problems is increasing attention due to their ability to learn and handle the nonlinearities and uncertainties of the systems. Since the last decade, different ANNs identification and filtering techniques have been applied in power systems and adaptive linear neuron networks (ADALINE) are actually widely used. More recently artificial neural networks have been introduced as a complement or an alternative to traditional control algorithms. The use of neural networks in control applications including process control, robotics, industrial manufacturing and aerospace applications, among others has recently experienced rapid growth. This paper proposes a new control method for shunt active power filter, which is based on ANN. An adaptive linear neuron (ADALINE) will be presented to estimate the reference compensation currents. The resultant compensation currents eliminate harmonics and reactive power compensation with a quick dynamic response. Theortical and expermental results in a good agreement