Wind and solar energy are increasingly vital for meeting clean renewable energy needs, with Brushless Doubly Fed Induction Generators gaining popularity due to their cost efficiency and reliability. A key challenge in integrating wind energy into the grid is ensuring low-voltage ride-through capability during faults and mitigating voltage fluctuations at the point of common coupling. Existing techniques, such as analytical models and evolutionary algorithms, aim to optimize reactive current compensation but suffer from low accuracy and high response times, respectively. This paper introduces a novel reinforcement learning-based current compensation technique for brushless doubly fed induction generators to address these limitations. The proposed reinforcement learning agent dynamically adjusts the reactive power to minimize voltage dips and stabilize the voltage profile during transient- and low-voltage ride-through faults, leveraging a reward function that penalizes deviations in voltage magnitude and increases in total harmonic distortion beyond 3%. By integrating reinforcement learning with traditional methods, the approach achieves faster and more adaptive compensation. Simulation results show that the reinforcement learning-based technique improves voltage recovery time by up to 50%, reduces total harmonic distortion by up to 44%, and minimizes current overshoot by up to 90% compared to state-of-the-art methods, enhancing the reliability and efficiency of wind energy systems.

DC motors are widely used in industries to provide mechanical power in speed and torque. The position and speed control of DC motors is receiving interest from the scientific community in robotics, especially in robotic arms, a flexible joint manipulator. The current research work is based on the position control of DC motors using experimental investigations in LabVIEW. The linear control strategy is applied to track the position and speed of the DC motor with comparative analysis in the LabVIEW platform and simulation analysis in MATLAB. The tracking error in the hardware setup based on LabVIEW programming is slightly greater than the simulation analysis in MATLAB due to the inertial load of the motor during steady-state conditions. The controller output shows the input voltage applied to the DC motor varies between 0 and 8 V to ensure minimal steady error while tracking the position and speed of the DC motor.

A normal operating study of the ITER PF AC/DC converter system commissioning the new Takagi–Sugeno (TS) fuzzy controller is presented in this work. The performance of the PI and TS fuzzy controllers is compared. The Mamdani fuzzy controller is studied and examined; however, it suffers from a huge number of sophisticated fuzzy sets and inference rules. The TS fuzzy controller on the ITER PF system is relatively simple and straightforward to put into practice. The proposed scheme’s current response verifies the step change. Furthermore, the investigation findings show that the TS fuzzy control method outperforms the traditional PI control.