| dc.description.abstract | 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. [...] | en_US |
