Voltage Optimization of PV/Wind Hybrid Renewable Energy System using Adaptive Neuro-Fuzzy Logic Technique

This research employs adaptive neuro-fuzzy inference system (ANFIS) technique to optimize the voltage of a PVWind hybrid renewable energy generating system with the aid of a Fuzzy Logic Charge controller. The controller increased the system’s effectiveness and ensured that power is delivered as efficiently as possible irrespective of weather conditions. The controller is used to track the maximum power point of the PV panels and that of the wind turbine and helps to distribute power among the hybrid system and to manage the charge and discharge current flow for performance optimization. MATLAB/SIMULINK software is used to model a typical hybrid PV-wind turbine system. This software has a number of PV-wind turbines that generate 100 kW output power (KW). The findings demonstrate that the fuzzy logic controller is reliable, effective, and quick to react to oscillations and can monitor the system’s peak power point at 0.25 seconds. From the simulation, the output voltages for phases A, B, and C are 874.407, 881.844, and 953.49 volts after optimization; and 794.91, 808.29, and 869.18 volts for phases A, B, and C before optimization. Similarly, significantly greater currents are produced by the optimized system than by the regular system. Phases A, B, and C of the optimized system has average current values of 82.74, 94.93, and 82.74 amperes each, whereas the phases A, B, and C of the conventional system has average current values of 81.93, 82.74, and 81.74 amperes each. System voltage is improved from an average value of 824.13V to 903.249V which shows a voltage gain of 79.12V. After optimization, the system’s efficiency increased from 68.99% to 78.32%, a significant  8.33% increase in efficiency.