Uzbekistan's rural regions experience continuing issues in energy access because of poor grid networks and variable renewable sources. The solution is small-scale wind turbines and energy storage. But the wind speeds and load demand are variable and thus intelligent control systems are needed for them to perform at their best.
This paper is an attempt to design an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to control a small wind power system with a battery storage unit. The controller will be intelligent to control the flow of power between the wind turbine, battery, and local loads. A MATLAB/Simulink model is created to simulate the reaction of the system to various wind and load conditions.
The results of the simulation prove that the ANFIS controller is better in stabilizing the voltage, reducing power fluctuations, and optimizing the battery charge–discharge cycle compared to other conventional PI and standalone fuzzy controllers. Environmental variability is effectively responded to by the system, making it more reliable and energy-efficient.
ANFIS control and wind–battery microgrid integration provide a feasible and expandable off-grid electrification solution to remote areas. This strategy promotes the renewable energy ambitions of Uzbekistan and offers an example of smart microgrid implementation in other resource-limited rural areas. The next steps would be in practical application and hardware verification.