Design and implementation of adaptive neuro–fuzzy inference system based control algorithm for distribution static compensator

Abstract

This article focuses on the design and implementation of a distribution static compensator using an adaptive neuro–fuzzy inference system based controller. The distribution static compensator is controlled to provide power quality improvement, such as power factor correction, harmonics compensation, load balancing, and voltage regulation. Active and reactive power fundamental components of load currents are extracted using d-q theory. A distribution static compensator is realized using a voltage source converter. Both simulation and experimental results prove the effectiveness of the control algorithm under non-linear loads. The adaptive neuro–fuzzy inference system based controller works satisfactorily for power factor correction and harmonics reduction under balanced as well as unbalanced load conditions. Test results clearly depict the dynamics of the performance of the system under steady state as well as dynamics under load change and load unbalancing.