This study proposes a novel deep feedforward neural network (DFNN)-based control for voltage sag prediction
and dynamic voltage restorer (DVR) integration with low-voltage ride-through (LVRT) to improve voltage stability and energy efficiency in wind turbine systems. Proposed approach precisely predicts sag duration, adaptively regulates DVR activation according to LVRT profile to avoid redundant compensation. This predictive
control reduces DVR operating time and energy consumption while maintaining voltage stability. Synthetic
datasets representing various grid conditions, including distorted voltage, enable DFNN to achieve a prediction
accuracy near 99 %, a time error margin of 0.005–0.02 s and a response time of 0.016 s. This precise prediction
improves DVR energy efficiency by nearly 30 % per long-duration fault. Compared to a support vector regression
(SVR) model, DFFN achieves 33.3 % faster response and lower error metrics. Simulation results validated in a
MATLAB/Simulink demonstrate effectiveness of proposed approach in enhancing LVRT capability and overall
grid efficiency. |
