Adaptive Neuro-Fuzzy Control of a Small Wind Turbine Integrated with Battery Storage for Remote Villages in Uzbekistan

Rural regions of Uzbekistan experience continuing issues of 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 this solution needs intelligent control systems to perform its 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 model of MATLAB/Simulink 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 at stabilizing the voltage, reducing the power fluctuations, and optimizing the battery charge-discharge cycle compared to other conventional PI and the standalone fuzzy controller. Environmental variability is effectively responded to by the system, making it more reliable and energy-efficient.

ANFIS control and wind–battery microgrid integration provides 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 on practical applications and hardware verification.