Variable Frequency Drive Optimization with Adaptive Neuro Fuzzy Inference System

Abstract

The output power of drive should be controlled to avoid stress on the advanced components in the input power system of the 1-∅ AC source; this is powered to the three-phase (3-∅) variable frequency drives (VFDs). To deal these issues, an integrated artificial neural network (ANN) and fuzzy logic control (FCL) named as adaptive neuro fuzzy inference system (ANFIS)-based VFD optimization is proposed to mitigate the stresses above the various parts of VFD such as input side, terminal block, direct current (DC) capacitor bus, current harmonics, torque ripple and speed of the induction motor (IM). In addition, the proposed ANFIS with the supervisory learning approach is utilized to regulate the speed with mitigated rise time and settling time of the VFD system. The proposed ANFIS model is simulated in MATLAB/Simulink environment and compared with several conventional VFD optimization The extensive simulated performance shows that the proposed ANFIS-based VFD has achieved better results than conventional VFD optimization techniques.