Low-voltage ride-through capability enhancement of wind energy conversion system using an ant-lion recurrent neural network controller

• Main Authors: Velappagari Sekhar, K Ravi
• Format: Article
• Language: English
• Published: SAGE Publishing 2019-09-01
• Series: Measurement + Control
• Online Access: https://doi.org/10.1177/0020294019858102

This paper proposes a hybrid controller to improve the low-voltage ride-through ability of the grid-connected wind energy conversion system. The hybrid controller is the joined execution of the ant-lion optimizer with the recurrent neural network called the ant-lion recurrent neural network. At voltage drop and fault conditions, the proposed control technique guarantees the low-voltage ride-through ability of the wind energy conversion system. The ant-lion optimizer in the perspective of objective function approach will be utilized in the offline manner to distinguish the optimal solutions from the accessible looking space and it makes the training dataset. Identifying the low-voltage ride-through ability, the ant-lion optimizer method considers voltage, current, and real and reactive power. Using these parameters, the objective function of the ant-lion optimizer strategy is described and explained. The recurrent neural network predicts the best possible control signals of grid-side and generator-side converters in light of the achieved dataset. The proposed method is utilized for system regulation and instability problem of voltage amid the fault conditions. In this way, the system’s low-voltage ride-through capability is upgraded and besides, the many-sided quality is diminished with the help of the proposed strategy. By applying the comparative investigation with the existing approaches, the proposed control procedure is actualized in the MATLAB/Simulink working stage and the performances are assessed.