A Hybrid Whale Optimization—Cuckoo Search Algorithm for Maximum Power Point Tracking in PMSG-Based Wind Turbine Systems

This study proposes an advanced optimization technique for maximum power point tracking (MPPT) in wind turbines (WTs) based on a permanent magnet synchronous generator (PMSG), which is crucial for maximizing energy extraction under varying wind conditions. Several MPPT strategies have been evaluated and compared, including neural networks (NNs), sliding mode control (SMC), the Whale Optimization Algorithm (WOA), and the Cuckoo Search Algorithm (CSA), to determine the most effective approach for optimizing power output and improving system efficiency. Emphasis is placed on identifying techniques that not only enhance energy capture but also reduce the complexity and cost of wind energy systems. To achieve this, the study introduces a novel hybrid algorithm that integrates the strengths of both WOA and CSA, leveraging their complementary exploration and exploitation capabilities. The proposed method aims to deliver improved tracking accuracy and faster convergence to the optimal power point. The algorithms were tested using a real wind profile from Djelfa, Algeria, a region characterized by semiarid climate and varied topography, to simulate realistic operational scenarios, providing accurate assessments of each MPPT strategy under true environmental conditions. The results obtained through MATLAB/Simulink simulations demonstrate that the newly developed hybrid WO–CSA strategy consistently outperformed others, delivering approximately 140 W more power than CSA and about 230 W more than WOA and NN at a wind speed of 10 m/s, while the SMC strategy exhibited the lowest performance, generating roughly 750 W less power compared to WOA and NN. By developing the new algorithm, the study contributes to the development of more efficient and reliable WT technologies.