Intelligent Hybrid Control of Wind Energy Conversion Systems for Enhanced Stability and Efficiency

Abstract

As wind energy becomes a bigger part of our power grid, we are running into a serious problem: wind is messy. Its unpredictable, nonlinear nature makes it incredibly difficult to keep the system stable and efficient. Old-school tools like PI and PID controllers are great for steady power, but they tend to fall apart when faced with sudden gusts or shifting weather. This study introduces a smarter way to handle that chaos using an intelligent hybrid control framework designed to keep the system steady and maximize energy capture, no matter what the wind is doing.

Our approach does not just rely on one tool; it combines fuzzy logic, neural networks, and evolutionary optimization to create a system that can "think" and adapt in real-time. By mixing rule-based logic with machine learning, this hybrid setup can automatically tweak things like rotor speed and blade pitch on the fly. We have backed this up with a deep dive into the math of turbine dynamics, proving that these intelligent hybrids are far more robust and efficient than the controllers We have used for decades. Ultimately, this work offers a new blueprint for building the kind of reliable, high-tech wind systems we need for the grids of tomorrow.