Nonlinear Control of Brushless Dual-Fed Induction Generator With a Flywheel Energy Storage System for Improved System Performance

Abstract

Managing fluctuations in wind energy to ensure a stable and reliable energy supply remains a significant challenge in the operation of wind generators. Various solutions have been developed to address this problem and mitigate the impact of wind energy fluctuations, such as the deployment of Flywheel Energy Storage Systems (FESSs). This study focuses on developing an advanced control strategy for a Brushless Dual-Fed Induction Generator (BDFIG) integrated with a Nonlinear Energy Storage System. In the first stage, a robust Sliding Mode Control (SMC)-based nonlinear decoupled control algorithm is designed to efficiently regulate BDFIG operation. The system is further enhanced with Optimal Torque Control (OTC) and Maximum Power Point Tracking (MPPT) to maximize wind energy extraction. In the second stage, a Synergetic Control (SC) strategy is implemented for FESS management, integrated seamlessly into the overall system. SC is selected for its ability to handle parametric and nonparametric uncertainties, offering a robust solution that ensures fast response times and asymptotic stability across varying operating conditions. To optimize parameter tuning within the control system, the Grasshopper Optimization Algorithm (GOA) is applied, ensuring optimal performance of the proposed framework. After optimization, the SMC settling time was significantly reduced from 0.7 seconds to 19.97 milliseconds, achieving a 96.9% improvement in response speed, while its steady-state error decreased from 0.48 to 0.06, marking an 87.5% reduction in tracking error. Similarly, the SC settling time improved from 0.85 seconds to 0.3 seconds, resulting in a 64.7% faster response, and its steady-state error was minimized from 0.044 to 0.03, enhancing accuracy by 31.8%. These enhancements contributed to a rapid dynamic response, reduced tracking error, and improved overall system stability under variable wind conditions. The proposed system was extensively simulated on MATLAB/Simulink under various wind generator operating conditions. These findings confirm the robustness and effectiveness of the proposed methodologies in delivering reliable and efficient energy management under different operating conditions.