Improvement of Power Quality Using Hybrid Active Filter with Artificial Intelligence Techniques

Abstract

In this paper, a shunt hybrid active filter (SHAF) is utilised for the improvement of power quality. Hybrid filters avail advantage of both passive filters and active filters by maintaining reactive power management and compensating harmonics related to different source and load conditions. In modern days, different artificial intelligence (AI) techniques are widely adapted for harmonics compensation. Here, a new intelligence technique, radial basis function neural network (RBFNN), is implemented along with conventional instantaneous power theory (p–q theory) for compensating harmonics and computing reference currents. The system performance is analysed using MATLAB/SIMULINK tool. The simulated results of the new technique are compared with those of the conventional neural controller with p–q theory. Results outcome indicates that the proposed techniques have better performance in improving power quality by reducing total harmonic reduction (THD) below IEEE standard.