Neural control of DVR for wind turbine grid fault mitigation with PIL validation

Abstract

Power quality issues that include voltage sag and swell challenge grid stability, not least for renewable energy systems such as wind turbines (WTs). Occurrence of these voltage disturbances impacts severely the performance of WT systems, compromising their fault ride-through (FRT) capabilities. This work investigates the application of an artificial neural network (ANN) as a controller mechanism for a dynamic voltage restorer, aimed at improving the FRT capabilities of a WT equipped with a permanent magnet synchronous generator. The approach includes employing series compensation to maintain the terminal voltage of the WT during fault conditions. This is performed by injecting voltage at the interface where the system connects to the grid, thus stabilizing the terminal voltage within the wind energy system. The control of the dynamic voltage restorer (DVR) is fundamental to improve the FRT capability. An ANN approach, as control technique is applied to drive the DVR. Training data used for ANN are obtained from a proportional-integral controller, and the proposed system is comprehensively modeled with MATLAB/Simulink. The proposed method demonstrates effective voltage restoration, under two fault scenarios: voltage sag and swell. Besides, the processor in-the-loop (PIL) test proves that the suggested control is practically implementable.