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

Abstract

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.

Ant Lion Optimization: Variants, Hybrids, and Applications

“…The classification results of FLNN-ALO is better than the standard FLNN. Likewise, Sekhar and Ravi in [177] proposed a hybrid controller which is a joined execution of the ant-lion optimizer with the recurrent neural network called the ant-lion recurrent neural network to improve the grid-connected wind energy low-voltage ride-through ability of the conversion system. Ansal [178] integrated ant lion optimizer with artificial neural network (ALO-ANN) to control dynamic voltage restorer (DVR).…”

Modelling and optimization of a pilot-scale entrained-flow gasifier using artificial neural networks

“…54 Velappagari Sekhar proposed an RNN-based controller to improve the low-voltage ride-through ability of the grid-connected wind energy conversion system. 55 Souza et al modelled a biomass gasification process in a fluidized bed gasifier based on the concepts of ANN to correlate between the composition of the produced gas and the characteristics of different biomasses for several operating conditions. 56 Pandey et al developed an ANN-based modelling approach to estimate the low heating value of the gasification products.…”

Robust fractional‐order super‐twisting sliding mode control to accurately regulate lithium‐battery / super‐capacitor hybrid energy storage system

“…In viewpoint of the sensitive industries and hybrid energy storage systems, the prevalent compensation strategies cannot present good results in case of the long‐time voltage disturbances caused by the load detachment, generator tripping, line disconnection, and capacitor‐bank energization 8,9 . The relevant compensators and regulators are generally based on the tap‐changers, uninterruptible power supplies, and DVR 10–12 .…”