Optimal Power Tracking for Grid-Connected Doubly Fed Induction Generator (DFIG) Wind Turbines Using OPO Algorithm

Enhancing Green Energy Development and Mitigating Emissions: A Dual-Focused Approach in Renewable Energy Initiatives The advancement of alternative green energy sources, such as wind energy, and the reduction of greenhouse gas emissions form a two-pronged strategy for renewable energy projects. The integration of power electronic-based controls has enabled Wind Energy Conversion Systems (WECS) to generate a consistent electric power output, irrespective of variations in the wind profile. As one of the most widely utilized renewable sources, wind energy plays a pivotal role in achieving sustainable power generation. This study canters on optimizing Perturb and Observe (P&O) algorithms, presenting a novel solution to address the shortcomings of current methods. Many existing approaches omit the initial tracking phase and assume an incorrect optimal generator speed, overlooking the inertia of WECS. The proposed Optimized Perturb and Observe (OPO) algorithm introduces a swift Maximum Power Point Tracking (MPPT) technique, employing innovative tracking methods to identify the optimal generator speed (Gs) in proximity to the Maximum Power Point (MPP). This enhances the efficiency and reliability of existing P&O algorithms. The research employs three control loops, incorporating Machine Learning (ML) techniques to optimize the P&O control. The primary focus is on analyzing and validating the performance of the proposed OPO algorithm for MPPT control systems. Leveraging the convergence capabilities of the optimization method and the global search capabilities of swarm intelligence, the research aims to maximize power output and contribute to the ongoing efforts in sustainable energy solutions.