to reduce fossil fuel consumption, which causes carbon dioxide emissions and global warming, renewable energy is gaining popularity. among various renewable energy sources, wind energy is one of the most cost-effective ways to generate electricity. numerous studies have been conducted to improve the performance of wind energy conversion systems (wecs) in various aspects. however, traditional control strategies employed in wecs often lead to lower efficiency, complicated implementation, complex system modeling, sophisticated drive circuit design, and suboptimal responses. this phd thesis presents a comprehensive exploration of cutting-edge techniques for optimizing wind energy conversion systems, unified by the application of a proposed multi-agent reinforcement learning (marl) method. the research is structured around three primary objectives, each contributing to the advancement of renewable energy technologies through the innovative use of marl. firstly, the thesis delves into the control of a neutral point clamped (npc) power converter employed in a direct-drive permanent magnet synchronous generator (pmsg)-based wecs. the focus is on enhancing power quality and meeting grid code requirements for total harmonic distortion (thd). traditional controllers like pi often struggle with parameter tuning and adaptability to varying operating conditions, resulting in suboptimal performance under dynamic and unbalanced scenarios. ai-based approaches, while more adaptive, typically require extensive offline training and detailed system modeling, making them less practical for real-time applications. the proposed approach eliminates the need for offline training and extensive system modeling, distinguishing itself from traditional machine learning (ml), neural network-based techniques, and pi-based methods. through simulations and comparative analysis, the effectiveness of the marl strategy is validated, particularly in handling unbalanced voltage sag scenarios. the integration of meta-learning to optimize the discount factor (df), a vital hyperparameter in rl-based approaches, further enhances the adaptability and convergence rate of the control system, ensuring power quality. afterwards, the research addresses the challenges in maximum power point tracking (mppt) for the wind energy conversion systems. traditional methods like perturb and observe (p&o) and incremental conductance are known for their slow dynamic response and susceptibility to steady-state oscillations around the maximum power point, especially under rapidly changing wind conditions. the proposed customized marl approach overcomes these limitations by employing multiple agents that work collaboratively, resulting in improved energy output and responsiveness to wind speed variations. [...]