6-phase DFIG for wind energy conversion system: A hybrid approach. (October 2022) - Chellaswamy, C.; Geetha, T.S.; Thiruvalar Selvan, P.; Arunkumar, A.
Highlights: A hybrid technique is proposed for a six-phase DFIG-based wind energy system. Deep reinforcement learning and quantum processes are combined together to form control strategies. It predicts the next systemic state and avoids local optimal solutions. The performance of the proposed method is tested using three different statistical indices. Good control performance on machine side and grid side converters is effectively obtained. Abstract: This research presents a novel wind power system based on a six-phase doubly-fed induction generator (DFIG). Optimization approaches are required to improve the efficiency of the traditional controllers. This study introduces a blended method for DFIG-based wind power transformation systems that combines quantum process and deep reinforcement learning (QPDRL) to improve control efficiency. It will be driven by using online control algorithms to eliminate the optimizing step and upgrade online control strategies. The proposed QPDRL can prevent local optimum solutions, forecast the future essential phase, and update DFIG-based wind power plants' regulation methods online. For two distinct scenarios, the QPDRL was contrasted with the proportional integral derivative (PID) controller, fractional-order PID, and reinforcement learning (for changeable air velocity, there are two types of arbitrary and step amplitudes). Matlab software was used to experiment. As air velocity variations exist, the findings revealed a 62% reduction in the DC link voltage ripples and a 99% reduction in speed overshoot with wind velocities overrun. Finally, comparing PID controls revealed a 42.15 percent reduction in grid current THD and an 11.38 percent reduction in the generator current.