Advanced control structure based on the Frequency Separation Principle of a Wind Energy Conversion System (WECS)

Abstract

This PhD thesis addresses the challenges posed by the instantaneous fluctuations in wind and the non-linearity of wind turbines, which hinder the full exploitation of wind power with high efficiency and reliability. The primary objective of this research is to propose innovative control strategies for Wind Energy Conversion Systems (WECS) that balance the maximization of energy efficiency with the minimization of mechanical stress through the reduction of electromagnetic torque ripple. These strategies are grounded in the principle of frequency separation, which distinguishes between the short-term and long-term variations in wind dynamics. This separation enables the development of a dual-loop control architecture, optimizing both the high-frequency and lowfrequency dynamics inherent in the system. In the high-frequency loop, various controllers, including Linear Quadratic Regulator (LQR) and H∞ controllers, are implemented to manage the dynamics induced by turbulent wind speed. In the low-frequency loop, fractional-order proportional-integral (FOPI) controllers are employed, with an advanced approach incorporating a filtered fractional-order proportional-integral (FOPIF) controller optimized using the Particle Swarm Optimization (PSO) algorithm. These controllers aim to mitigate electromagnetic torque ripple, also known as the chattering problem, and ensure efficient Maximum Power Point Tracking (MPPT), thereby enhancing system performance under variable wind conditions. The proposed control strategies not only maximize the energy production of wind turbines but also reduce electromagnetic torque ripple, thereby minimizing mechanical stress, extending the operational lifespan, and lowering maintenance costs. By improving the dynamic behavior and reliability of the WECS, this research contributes significantly to advancing control technologies in wind energy systems, promoting the growth of efficient, scalable, and resilient renewable energy solutions. The findings of this work support the global transition toward sustainable energy, offering valuable insights into optimizing wind energy systems under challenging environmental conditions.