A Hybrid Quantum Neural Network and Enhanced Sparrow Search Algorithm for Efficient MPPT in DFIG‐Based Wind Energy Conversion Systems

Harmonic distortion reduction, control trade‐off compromise, and rotor speed optimization to maximize power extraction in variable wind conditions were also prior issues with the Wind Energy Conversion System (WECS), which is designed around Doubly‐Fed Induction Generators (DFIG). In an attempt to tackle these problems, sophisticated algorithms such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) were developed. Although these algorithms enhanced performance, they frequently encountered difficulties with energy tracking precision and quick swings in wind speed (WS). To solve these restrictions, hybrid algorithms and improved control procedures were suggested. An efficient hybrid solution is provided for the quick and effective Maximum Power Point Tracking (MPPT) in DFIG‐based WECS using an efficient technique. The hybrid approach that is being given combines the use of the Quantum‐based Neural Network (QNN) with the Enhanced Sparrow Search approach (ESSA). The MPPT technique and the loss reduction approach which is based on variations in the generator speed are used to estimate the set point direct axis and quadrature axis current parameters of the WECS. This appropriate control has increased the effectiveness of the recommended method. Lastly, the suggested model is implemented using the MATLAB platform, and its effectiveness is evaluated in relation to alternative methods. According to the investigation, the Field‐Oriented Control (FOC)‐PI controller's Total Harmonic Distortion (THD) of 1.33% satisfies IEEE‐519 requirements. On the other hand, the suggested method lowers the THD to 0.27%.