Performance enhancement of wind energy conversion systems using PMSG with backstepping and ANN-based MPPT control

This paper investigates the performance enhancement of wind energy conversion systems (WECS) using a Permanent Magnet Synchronous Generator (PMSG) specifically designed for standalone, fixed-pitch, variable-speed wind turbines. The study focuses on controlling the PMSG using Backstepping control which enables Maximum Power Point Tracking (MPPT) using the Tip Speed Ratio (TSR) method. Initially, a Proportional-Integral (PI) controller was implemented for regulating the generator speed. However, this approach encountered significant limitations, particularly in managing speed overshoot and responsiveness under fluctuating wind conditions. To address these issues, a neural network controller was introduced as a replacement for the conventional PI controller. This neural network controller provides an adaptive control mechanism capable of dynamically adjusting to change, thereby eliminating overshoot and greatly enhancing response speed and overall system stability. This work provides a reliable and efficient control solution where was the proposed control strategy was rigorously evaluated through numerical simulations in Matlab/Simulink, which confirmed its ability to stabilize the system and achieving both steady-state and dynamic optimization of wind energy conversion systems.