Voltage Stability Enhancement in DFIG Systems Through IOT‐Based Adaptive Control Using Neural Network Technique

Doubly fed induction generator (DFIG) systems can enhance voltage stability through Internet of Things (IoT)‐based monitoring and control, enabling effective analysis and proactive grid management. However, voltage stability in DFIG systems with IoT can be affected by communication delays that slow control actions, reducing the effectiveness of voltage regulation. This manuscript presents a novel framework for voltage stability enhancement in DFIG systems through IoT‐based adaptive control. The proposed method integrates the capabilities of logarithmic differential convolutional neural network (LDCNN) with a reindeer cyclone optimization (RCO) algorithm, forming a model termed LDCNN–RCO. The primary goals of the proposed technique are to maximize efficiency, develop system stability, develop forecasting accuracy, and minimize estimation errors. LDCNN is employed to forecast the dynamic behavior of DFIG‐driven wind turbines (WTs) throughout voltage dips, whereas RCO optimizes the distribution of reactive power. The proposed method demonstrates better performance than existing techniques, including Tasmanian devil optimization algorithm with multiresolution sinusoidal neural network (MSNN–TDO), artificial neural network (ANN), and sparrow search algorithm (SSA), as evaluated and compared using the MATLAB platform. The proposed technique achieves a lower torque ripple of 0.13%, response time of 1 ms, and higher accuracy of 99.3%, along with a higher computational efficiency of 97%, indicating superior performance and effectiveness compared to existing methods. The proposed LDCNN–RCO framework significantly enhances voltage stability and control efficiency in DFIG systems, outperforming existing methods in accuracy and response time.