PSO-Based MPPT Algorithm of a Wind Turbine Generator

In recent years, the imperative to harness renewable energy sources has gained unprecedented momentum, driven by the dual challenges of climate change and decreasing fossil fuels. Among various sustainable technologies, wind energy stands out as a promising alternative, offering substantial potential for sustainable power generation. However, the effectiveness of renewable systems such as wind energy conversion systems (WECS) is significantly impacted by the inherent variability of wind speeds. To mitigate these challenges and optimize energy output, maximum power point tracking (MPPT) approaches are considered essential tools. These algorithms facilitate the extraction of the maximum available power from wind turbines, adapting to changing environmental conditions through advanced computational techniques. Optimization techniques are a powerful means to deal with complex challenges in renewable energy systems. However, their effectiveness often relies heavily on the accuracy of the mathematical model used for the generator and factors like climatic situations. To address these challenges, researchers have developed various unconventional optimization approaches, often referred to as “smart” methods, which are based on meta-heuristics. These intelligent techniques are widely applied in areas such as system modelling, identification, and control. Their popularity stems from the flexibility to variations in system conditions and the robustness against disturbances and modelling inaccuracies. The integration of intelligent methods into MPPT strategies not only enhances system performance but also promotes the reliability and development of wind energy systems. This chapter will explore the innovative approaches and technologies that underpin MPPT in WECS, highlighting their importance in the transition towards a sustainable energy future.