Artificial Intelligence Integrated Fractional Order Control of Doubly Fed Induction Generator-Based Wind Energy System

Abstract

This paper proposes an artificial intelligence integrated (AI) fractional order robust control for a DFIG based wind energy conversion system. To reduce the chattering phenomena in the excitation signal, fuzzy system is employed for the adaptive adjustment of the discontinuous control gain while preserving the robustness of the closed-loop system. The stability of the closed loop system with fuzzy fractional order robust control (FFORC) is ensured using fractional order Lyapunov system. The proposed FFORC control scheme is tested using processor in the loop (PIL) experiment.MATLAB/Simulink environment is used to emulate DFIG based wind energy system and a Texas Instrument (TI) DSP320F37D processor is used for interfacing the proposed control scheme with the emulated DFIG model in Simulink environment. System performance under the proposed FFORC scheme is compared with classical sliding mode control(SMC).The experimental results justifies the superiority of the proposed FFORC control scheme under all test conditions.Under ideal condition and with the proposed FFORC control scheme, the speed tracking error is approximately zero while with SMC method the peak tracking error is 0.4 radian/s. Similarly the active and reactive powers tracking is smooth with the proposed control system, while with SMC method the reactive power oscillates on both sides of the reference and it reaches 0.01 kVAR on positive side and -0.01kVAR on the negative side of the plot.Under parameters variation, system with FFORC control scheme offers minimum steady state error which is about 0.01 radian/s, while in case of SMC with saturation function a peak value of 0.6 radian/s is recorded. In case of SMC with sgn function, the speed tracking error is around 0.1 radian/s.Moreover the proposed FFORC scheme exhibits minimum chattering.