A Novel Variable Step Incremental Conductance Maximum Power Point Tracking Algorithm based on ANFIS Controller for Grid Photovoltaic Systems

Photovoltaic (PV) generating devices, which use solar energy, have seen widespread use in modern power grids. Improving the efficiency of the PV system is essential for reaching full potential. Continuously collecting the greatest power from the PV arrays when environmental circumstances change is the key to realising this advantage. To optimise the performance of the PV system as a whole, maximum power point tracking (MPPT) must be implemented. INC, perturb-and-observe, fractional short-circuit current, fractional open-circuit voltage, and hill climbing are some of the most used MPPT techniques. Many different approaches to MPPT for PV system control have emerged in response to developments in artificial intelligence technology. However, the efficiency and resilience of such approaches are low. The primary goal of this work is to increase the efficiency of maximum power point tracking (MPPT) by the use of variable step size incremental conductance. Fuzzy logic-based step size adjustment for incremental conductance (INC) maximum power point tracking (MPPT) for PV. This research calculates voltage step magnitude based on power-voltage relation steepness. A unique treatment that introduces five effective regions around the point of maximal PV production achieves this. A fuzzy logic system adjusts the duty cycle's step size using the fuzzy inputs' placements in the five regions. The current-voltage ratio and its derivatives determine the fuzzy inputs while appropriate membership functions and fuzzy rules are built. The suggested method's advantage is that it allows the MPPT efficiency to be adjusted by changing the size of the incremental conductance step. The main controller used is Fuzzy Logic Controller, but this controller may not achieve the required parameters. Many rules are there, that are needed to be follow while implementing the work. And also, does not adaptable for all the varying parameters in the system. To overcome this problem, a magnified controller known as ANFIS Controller. This ANFIS Controller will replaces the Fuzzy Logic Controller in the controlling topology. This controller works by using both ANN and FLC based rules and characteristics. By using this controller, we can be improving the dynamic response of the system and the tuning of membership functions can be possible to obtain the required output. It also produces stable signals in the system. The transient behaviour of the system can be improved. The performance results of this extension method can be evaluated by using MATLAB/SIMULINK environment.