An Antlion Optimizer Trained ANFIS Control Scheme to Improve LVRT Characteristics of Grid Connected PMSG

Abstract

Low voltage ride-through (LVRT) is one of the essential grid code requirements for interconnecting the wind energy conversion system. The PMSG with full scale back to back conversion has attained a lot of curiosity due to its outstanding performance as per in the LVRT during grid disturbance like a fault. The conventional controllers line PI along with coordinated control schemes, even though performs good, with dynamic changes in grid parameters especially at the time of grid faults condition cannot effectively control the system. This paper aim is to present a hybrid system development by artificial intelligence (AI) methods to improve LVRT acquirement. Both Adaptive Neuro-Fuzzy Inference System (ANFIS) and Ant-Lion Optimization (ALO), which is current intelligent optimization techniques were employed here to support the connected system and its performance. According to the simulation findings, which exhibit explicitly the improved LVRT capability of the grid-connected PMSG wind generator. The results of ALO and hybrid ALO-ANFIS are compared. The proposed hybrid control scheme, ALO trained ANFIS improving the LVRT grid code requirement considering active, reactive power and DC link voltage than ALO.

 Keywords: PMSG, LVRT, ANFIS-ALO, generator side converter, grid side converter.