Real-Time Validation of Intelligent Super-Twisting Sliding Mode Control for Variable-Speed DFIG Using dSPACE 1104 Board

Abstract

The super-twisting sliding mode controller (STSMC) is considered one of the simplest and easiest to implement nonlinear controls, as it can be easily applied in industrial systems. Using this controller leads to reducing the phenomenon of chatter and significantly increasing the durability of the systems. However, this controller has several drawbacks and problems, as its use does not lead to improving the systems as required. In this study, an idea has been proposed based on the use of neural networks in order to overcome the defects and problems of the STSMC strategy. This proposed strategy is used to increase the durability and improve the characteristics of an energy system based on wind turbines using a dual induction generator. The latter is controlled by direct power control (DPC). The proposed controller was used to control energies, where the pulse width modulation strategy was used to convert voltage reference values into pulses to operate the rotor side converter. Therefore, simplicity, durability, outstanding performance, ease of implementation, few gains, and low cost are among the most prominent features of the proposed intelligent STSMC technique and control. The MATLAB environment was used first to implement the proposed energy system under different working conditions using variable wind speed, where the proposed controller was compared with the performance of the STSMC technique. The proposed intelligent STSMC technique was implemented experimentally using the dSPACE 1104 Card to verify the performance and the validity of the simulated results. The experimental results show the effectiveness and ability of the proposed controller to improve the characteristics of the DPC strategy and the energy system as a whole.

Keywords

• Neural algorithms
• super-twisting-direct power command
• dSPACE 1104 Card
• doubly-feed induction generator