Abstract
Abstract Power generation losses arise in doubly fed induction generator (DFIG) system due to grid faults. The system’s protection should ensure that the wind turbine (WT) generator meets the grid requirements through a low voltage ride through (LVRT) technique. This article proposes the feed-forward neuro-second order sliding mode (FFN-SOSM) control for the LVRT enhancement under voltage sag. This controller operates with the levenberg marquardt (LM)-super twisting (ST) algorithm for the uncertainties of the DFIG system. The LM-ST algorithm-based proposed controller is subjected to stability analysis. The advantages of the proposed controller are that it reduces the system parameter’s peak values and harmonic distortion of the system during grid disturbance. The performance of the proposed controller is compared with existing controllers in the literature with the help of MATLAB/SIMULINK. The hardware-in-loop (HIL) validates these simulation results performed on the OPAL-RT setup. Based on the studies, it is found that the proposed controller enhances the LVRT performance of the WT-DFIG system under transient conditions.
Highlights
Power generation losses arise in doubly fed induction generator (DFIG) system due to grid faults
The performance evaluation of the DFIG-based wind turbine (WT) generator is carried out using the FFN-SOSM controller, and Figure 10 shows the results under no fault condition
The perspective of the low voltage ride through (LVRT) enhancement is analysed through the proposed controller, and the conclusions are drawn as follows: (i) The HIL (OPAL-RT 4510) results are closely matched with the simulation results, which are validation to the proposed controller under the voltage sag
Summary
Abstract: Power generation losses arise in doubly fed induction generator (DFIG) system due to grid faults. This article proposes the feed-forward neuro-second order sliding mode (FFN-SOSM) control for the LVRT enhancement under voltage sag. Error signals between references and measured rotor currents are presented Fractional order uncertainity is integrated into control law to compensate parameter uncertainties Extra input is added to compensate the estimation error Single ANN identifies the fault type Conventional switching table by voltage selector based on the ANN Centre of Gaussian member function changes as per the crisp values According to fuzzy rules, PI gain is adapted with error Sliding condition makes surface an invariant set surface. In connection to resolving these drawbacks, in this article the feed-forward neuro second-order sliding mode (FFN-SOSM) control is proposed This is a rotor side controller, which enhances the LVRT technique by reducing the system parameter’s peak values and minimizes the system’s harmonic distortion during voltage sag.
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