Robust Control Techniques for Electrical Machines in Wind and Solar Energy Applications

Abstract

An investigation analyzes performance-enhancing methods for renewable energy system electrical equipment, especially solar and wind power generators, through strict management systems. Solar and wind power technologies increase in importance which leads to the necessity of complex control systems to address unpredictable characteristics in these power systems. Robust control strategies, including H∞ control, Sliding Mode Control (SMC), and Model Predictive Control (MPC), offer effective solutions for managing variability of environmental conditions, like fluctuating wind speeds and solar irradiance. The mentioned techniques enable the achievement of maximum energy conversion performance together with fault detection systems and reliable system operation. The essay explores both adaptive control systems that adapt through methods like fuzzy logic with neural network systems to enhance dynamic operating performance in their application to power systems. The paper concludes by highlighting future research directions, including the integration of machine learning with robust control techniques to enhance real-time adaptability and scalability in large-scale renewable energy systems.