Small-scale wind turbines (SWTs) are a hopeful way to bring stable energy to Uzbekistan's remote towns. Many rural homes are still only poorly connected to the national grid or not connected at all. In these kinds of systems, proportional–integral (PI) control is not enough to make sure of tight DC-bus regulation because the wind is not always the same, the turbine and generator do not always move in the same way, and the DC loads change all the time. This paper suggests an intelligent, metaheuristic-optimized driver for a small-scale DC wind energy conversion system. The goal is to achieve a stable 48 V, 1 kW DC supply for bringing electricity to rural areas. The setup that was looked at has a horizontal-axis SWT connected to a permanent-magnet DC generator and a DC–DC buck converter that supplies a 48 V DC bus. The aerodynamic, electrical, and power electronic subsystems are modeled in MATLAB/Simulink using real wind data from the Bukhara region in 2024 (NASA POWER), along with hub-height adjustment and IEC 61400-1 Kaimal-spectrum turbulence reconstruction to show how the wind really changes speed at high frequencies. Using an ANFIS-based SWT model that has already been proven to work in rural Uzbekistan, this study adds a Grey Wolf Optimizer-tuned Adaptive Neuro-Fuzzy Inference System (GWO–ANFIS) controller that works better and is more stable than a well-tuned PI controller.
The suggested controller uses an ANFIS structure that has two inputs, the DC voltage of the generator and the power of the turbine, and a single output, the buck converter's duty-cycle command. Generalized bell-shaped membership functions and a Sugeno-type rule base can also be used. The Grey Wolf Optimizer does a global search over the premise and consequent parameters. To obtain the best results, the optimization reduces a mixed objective function that includes DC-bus voltage tracking error, settling time, and RMS voltage noise. This builds power-quality standards directly into the learning process. The controller's performance is tested with step changes in wind speed, IEC-compliant unstable wind profiles, and step-changing DC loads that are typical of what people use in rural areas.

The simulations show that the GWO–ANFIS controller performs a lot better than the PI controller in all situations. GWO–ANFIS demonstrates faster settling and smaller delay when wind speed changes and it keeps the 48 V reference over a wide input voltage range (about 168–530 V). When there is a lot of wind, the improved driver cuts down on DC-bus voltage noise and improves disturbance avoidance. During load steps, the DC voltage stays very stable, and the output current follows the power demand without oscillating.

These results show that using neuro-fuzzy control along with metaheuristic optimization based on nature creates a strong AI-driven control strategy for small-scale DC wind energy systems. The suggested GWO–ANFIS processor improves the voltage, responds more quickly, and is more reliable without adding to the complexity of the hardware. This makes it a good choice for a low-cost, stable DC micro-power source in rural Uzbekistan and other places with low to medium wind.