Cascaded Neural Network-Based Power Control for Enhanced Performance of Doubly Fed Induction Generator-Based Wind Energy Conversion Systems

The increasing penetration of wind energy is a key enabler of the global transition toward low-carbon and sustainable power systems. However, ensuring high efficiency, power quality, and operational reliability under variable wind and grid conditions remains a critical challenge for doubly fed induction generator (DFIG)-based wind energy conversion systems. Conventional direct power control (DPC) strategies based on proportional–integral (PI) regulators are simple and widely implemented, yet their performance degrades in the presence of nonlinear system dynamics, parameter uncertainties, and rapid wind speed fluctuations—factors that directly affect energy yield, component lifetime, and grid stability. To enhance the sustainability and resilience of wind power generation, this study proposes a cascaded neural network-based control architecture for DFIG-driven systems. The outer neural control loop regulates active and reactive power references to optimize energy capture and support grid requirements, while the inner neural loop ensures fast and precise tracking by generating appropriate control signals for the rotor-side converter. Leveraging their adaptive learning capability, the neural controllers effectively model nonlinear dynamics and compensate for uncertainties in real time. Compared with the conventional DPC-PI scheme, the proposed approach achieves improved dynamic response, reduced power and electromagnetic torque ripples, enhanced disturbance rejection, and greater robustness under varying wind and grid conditions. These improvements contribute to sustainable energy production by increasing conversion efficiency, reducing mechanical stress, minimizing maintenance requirements, and extending turbine service life. Furthermore, improved reactive power control enhances grid integration and supports stable operation in renewable-dominated power systems. Simulation results validate the superior performance of the cascaded intelligent control strategy. The findings demonstrate that advanced adaptive control techniques can play a significant role in strengthening the reliability, efficiency, and long-term sustainability of wind energy systems, thereby supporting global decarbonization goals and the broader transition to sustainable energy infrastructures. Future work will focus on real-time implementation, stability assessment, and experimental validation to facilitate practical deployment.