Maximum power extraction strategy for variable speed wind turbine system via neuro-adaptive generalized global sliding mode controller

Abstract

The development and improvements in wind energy conversion systems (WECSs) are intensively focused these days because of its environment friendly nature. One of the attractive development is the maximum power extraction (MPE) subject to variations in wind speed. This paper has addressed the MPE in the presence of wind speed and parametric variation. This objective is met by designing a generalized global sliding mode control (GGSMC) for the tracking of wind turbine speed. The nonlinear drift terms and input channels, which generally evolves under uncertainties, are estimated using feed forward neural networks (FFNNs). The designed GGSMC algorithm enforced sliding mode from initial time with suppressed chattering. Therefore, the overall maximum power point tracking (MPPT) control is very robust from the start of the process which is always demanded in every practical scenario. The closed loop stability analysis, of the proposed design is rigorously presented and the simulations are carried out to authenticate the robust MPE.