Direct adaptive control of wind energy conversion systems using Gaussian networks.

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy

Background:

• Grid-connected wind energy conversion systems (WECS) exhibit complex nonlinear dynamics.
• Effective control is crucial for stable and efficient power generation from wind turbines.

Purpose of the Study:

• To propose a direct adaptive control strategy for WECS.
• To address the challenges posed by nonlinear characteristics in windmills and electric generators.

Main Methods:

• A hybrid control approach combining a radial basis function network-based adaptive controller and a supervisory controller.
• Lyapunov stability analysis to derive control laws and adaptation mechanisms.
• Application to a typical turbine/generator pair for validation.

Main Results:

• The proposed direct adaptive control strategy effectively drives tracking error to zero.
• The supervisory controller ensures stability when neural network approximation properties are uncertain.
• Demonstrated feasibility and performance on a standard WECS model.

Conclusions:

• The developed adaptive control strategy offers a robust solution for WECS control.
• The combination of adaptive neural networks and supervisory control enhances system stability and performance.
• This approach is viable for practical implementation in grid-connected wind energy systems.