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Robust Nonlinear Adaptive Control for Power Quality Enhancement of PMSG Wind Turbine: Experimental Control Validation
Due to the intense penetration of wind energy into the power grid, grid quality and stability have become a crucial necessity in this type of power generation. It is in this context that this article has just designed an Adaptive Nonlinear Control strategy applied to the Permanent Magnet Synchronous Generator (PMSG) of 1.5 MW power, in order to generate good quality and cleanly usable energy. Interestingly, this robust control algorithm mainly uses the Lyapunov stability theory, which ensures the stability of the Wind Energy Conversion System (WECS), and therefore offers excellent results in the presence of system parametric uncertainties and changes in the elements of the external environment. To this end, the methodology followed in this in-depth study focuses on the application of the Adaptive Backstepping Control algorithm for WECS by exploiting the MATLAB/Simulink toolbox. The theoretical study and simulation of the WECS was supported by the Processor-in-the-Loop (PIL) implantation of the control in the dSPACE DS1104 embedded board to approve the effect of the control in terms of robustness against different wind profiles and parametric changes. ST-LINK communication is used to connect the embedded board and the host computer. The results obtained revealed a fast response of the different signals, a practically low ripple rate of the order of 0.1% and minor overshoots for the different electrical quantities. Operation with a unity power factor is well ensured via this control strategy. Therefore, the adaptive control applied to the WECS has verified the high performance offered and benefits from additional robustness properties.
Robust Nonlinear Adaptive Control for Power Quality Enhancement of PMSG Wind Turbine: Experimental Control Validation
Due to the intense penetration of wind energy into the power grid, grid quality and stability have become a crucial necessity in this type of power generation. It is in this context that this article has just designed an Adaptive Nonlinear Control strategy applied to the Permanent Magnet Synchronous Generator (PMSG) of 1.5 MW power, in order to generate good quality and cleanly usable energy. Interestingly, this robust control algorithm mainly uses the Lyapunov stability theory, which ensures the stability of the Wind Energy Conversion System (WECS), and therefore offers excellent results in the presence of system parametric uncertainties and changes in the elements of the external environment. To this end, the methodology followed in this in-depth study focuses on the application of the Adaptive Backstepping Control algorithm for WECS by exploiting the MATLAB/Simulink toolbox. The theoretical study and simulation of the WECS was supported by the Processor-in-the-Loop (PIL) implantation of the control in the dSPACE DS1104 embedded board to approve the effect of the control in terms of robustness against different wind profiles and parametric changes. ST-LINK communication is used to connect the embedded board and the host computer. The results obtained revealed a fast response of the different signals, a practically low ripple rate of the order of 0.1% and minor overshoots for the different electrical quantities. Operation with a unity power factor is well ensured via this control strategy. Therefore, the adaptive control applied to the WECS has verified the high performance offered and benefits from additional robustness properties.
Robust Nonlinear Adaptive Control for Power Quality Enhancement of PMSG Wind Turbine: Experimental Control Validation
Hassna Salime (author) / Badre Bossoufi (author) / Youness El Mourabit (author) / Saad Motahhir (author)
2023
Article (Journal)
Electronic Resource
Unknown
Adaptive Nonlinear Control , permanent magnet synchronous generator , wind energy conversion system , dSPACE DS1104 embedded board , Processor-in-the-Loop implantation , Environmental effects of industries and plants , TD194-195 , Renewable energy sources , TJ807-830 , Environmental sciences , GE1-350
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