Design and real time implementation of intelligent MPPT and pitch control strategies for grid-connected WECS-PMSG under highly dynamic and uncertain wind conditions

Abstract

Achieving high efficiency and reliable operation in wind energy conversion systems remains a critical challenge, particularly under fluctuating wind conditions, where energy extraction must be carefully balanced with system stability. This paper proposes an intelligent hybrid control strategy that integrates maximum power point tracking with pitch angle control, two fundamental mechanisms for optimal operation of wind turbines based on permanent magnet synchronous generators, where accurate control of rotor speed and electromagnetic torque is critical. In this study, conventional maximum power point tracking and pitch control algorithms are enhanced using artificial neural networks. The main objective is to ensure safe, efficient, and robust operation under all wind conditions and across all operating zones. The proposed control approach was first developed and evaluated through detailed simulations in the MATLAB/Simulink environment, and then validated through real-time implementation on a dSPACE DS1104 platform. The simulation tests were conducted under two operating scenarios: step-varying wind profile and real measured wind data. Compared with conventional maximum power point tracking and pitch control based on a proportional–integral regulator, the proposed method significantly improves dynamic performance and overall system efficiency by up to 30%. The results demonstrate notable performance enhancements, including a faster transient response (settling time < 150 ms), high tracking accuracy (steady-state error < 2%), and substantial reductions in electromagnetic torque, DC-link voltage, and power ripples (approximately 40%), as well as a significant decrease in current total harmonic distortion (around 50%). These findings confirm that the artificial neural network controllers effectively enhance dynamic performance while ensuring stable and high-quality power injection into the electrical grid, making it a highly promising solution for wind energy conversion systems. • Proposes an intelligent hybrid control strategy combining MPPT and Pitch Angle (PA) control for enhanced WECS efficiency. • Integrates Artificial Neural Network (ANN) algorithms to enable adaptive and robust performance under fluctuating wind conditions. • The proposed control is modeled and tested in MATLAB/Simulink and validated experimentally using the dSPACE DS1104 platform. • Evaluations include both step-change and real wind speed profiles for realistic performance assessment. • Achieves up to 30% improvement in response time, overshoot reduction, tracking accuracy, and power quality compared with conventional PI-based methods.