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ANFIS Controller Design Using PSO Algorithm for MPPT of Solar PV System Powered Brushless DC Motor Based Wire Feeder Unit

In this study an improved control scheme for autonomous photovoltaic (PV) generator fed wire feeder unit (WFU) of arc welding machine using a brushless DC (BLDC) motor is developed. As power generated from PV energy systems always depends on varying weather conditions, to get maximum output power from PV generator at every insolation variation, Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT controller is combined with particle swarm optimization (PSO) algorithm to track the MPP of the PV generator. The suggested hybrid technique identifies and tracks the MPP and provides accurate and fast response of an autonomous PV wire feeder unit (PV-WFU). To maintain the welding wire speed constant under varying conditions, a fractional-order PI (FOPI) speed controller based on hysteresis current control (HCC) scheme is adopted to drive the BLDC motor of WFU. A MATLAB/SIMULINK model is developed for this purpose and results are presented. The simulations show that the proposed hybrid PSO-ANFIS based on MPPT controller has been able to control the PV output power around the maximum point at all weather condition scenarios. In addition, the proposed FOPI controller successfully tracked the welding wire speed for WFU over traditional PI controller.