Artificial Neural Fuzzy Based MPPT for High Gain Zeta Converter for Standalone PV Application with Galvanic Isolation

The setting up of photovoltaic (PV) systems is continuously rising globally since they are the primary means of accessing the electrical grid. Photovoltaic systems require a great deal of work to operate at their best. The dispersed sources are connected to the load by power electronics connections. However, electricity quality is declining due to PV's inconsistent nature. Consequently, the goal of this paper is to get stable DC-link voltage of the ZETA Converter using the adaptive network based fuzzy interfering system Maximum power point tracking (ANFIS based MPPT) approach. By utilizing a ZETA converter in this proposed method, the output voltage and current ripple content is decreased and the voltage gain is enhanced. This method offers galvanic security to the source and load by removing the opposite current. To reduce the unpredictable behavior of PV and stabilize the converter's output voltage, a sophisticated MPPT is employed. This helps to maintain DC-link voltage without aberrations or ripples from the converter. It is verified that the proposed system works using MATLAB simulation data. The comparison of the converters shows the greater efficiency of Zeta Converter as 94% and the Maximum power tracking efficiency of ANFIS as 93%.