Grid-tied Two PV LLC Converter with Dual MPPT Algorithm based on the Adaptive Neuro Fuzzy Interface System (ANFIS)

This paper proposes a dual Maximum Power Point Tracking (MPPT) algorithm using the Adaptive Neuro Fuzzy Interface System (ANFIS) to harvest maximum power from a grid-tied phase-shifted dual-input LLC converter. The algorithm can simultaneously extract maximum power from two Photovoltaic (PV) panels even under dynamic weather conditions and partial shading. The dual MPPT algorithm generates duty cycle and phase shift to regulate the power flow using combined pulse width modulation (PWM) and phase-shift modulation (PSM) techniques. Zero Voltage Switching (ZVS) in all primary switches and Zero Current Switching (ZCS) in the secondary diodes are achieved over the entire range of input and load conditions. The proposed ANFIS model uses the LLC converter’s input-output data set for each PV panel to train the neural network while the fuzzy rules ensure the optimum output using membership functions. On the other hand, a grid controller maintains the fixed DC link as well as ensures grid power injection. Derivation of this dual-MPPT algorithm followed by the verification of the proposed closed-loop system is presented here.