PROJECT TITLE :

Adaptive Neurofuzzy Inference System Least-Mean-Square-Based Control Algorithm for DSTATCOM

ABSTRACT:

This paper proposes the real-time implementation of a 3-phase distribution static compensator (DSTATCOM) using adaptive neurofuzzy inference system least-mean-sq. (ANFIS-LMS)-based management algorithm for compensation of current-connected power quality problems. This algorithm is verified for numerous functions of DSTATCOM, such as harmonics compensation, power factor correction, load balancing, and voltage regulation. The ANFIS-LMS-primarily based management algorithm is used for the extraction of fundamental active and reactive power elements from nonsinusoidal load currents to estimate reference offer currents. Real-time validation of the proposed control algorithm is performed on a developed laboratory prototype of a shunt compensator. The $64000-time performance of shunt compensator with ANFIS-LMS-based management algorithm is found satisfactory underneath steady-state and dynamic load conditions. The performance of the proposed control algorithm is also compared with mounted-step LMS and variable-step LMS (VSLMS) to demonstrate its improved performance.