Hybrid MPPT Strategy: Optimizing SEIG Performance in Wind Energy Installation

This research delves into the comparative effectiveness of two MPPT strategies within wind energy conversion systems that utilize a self-excited induction generator (SEIG). The first strategy is a hybrid approach that integrates variable step size perturb and observe (VSS-P&O) with feed-forward artificial neural network (FF-ANN) control, while the second is the conventional FF-ANN-based MPPT method. We investigate the limitations associated with VSS-P&O, such as its susceptibility to environmental conditions and wind speed fluctuations, which can result in suboptimal tracking accuracy. By combining the duty cycles produced by both techniques and applying them to a DC-DC boost converter, the hybrid MPPT method offers a promising solution to alleviate these limitations. Through extensive simulations conducted in MATLAB/Simulink under various operating conditions, we assess the performance of both strategies. Our analysis indicates that the hybrid approach not only enhances tracking accuracy and convergence speed but also improves system stability compared to the standalone FF-ANN-based MPPT method. These findings highlight the potential of hybrid MPPT solutions in overcoming the constraints of traditional variable step size P&O algorithms, thus contributing to the development of more efficient and dependable wind energy conversion systems (WECSs).