Model predictive control of DFIG-based wind turbine
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Renewable energy as a green source of energy is clean, accessible and sustainable. Due to advanced control, lower cost and government incentives, wind energy has been the largest growth among other renewable sources. With fast growing in the new generation of generators, Doubly Fed Induction Generators (DFIGs) became more popular because of handling a fraction (20-30%) of the total system power which leads to reduce the losses in the power electronic equipment and also their ability in decoupling the control of both active and reactive power. In addition, DFIGs have better behaviour in system stability. Therefore, in this study, the model of one-mass wind turbine with DFIG is represented by a third order model. Model Predictive Control (MPC), as a powerful control method to handle multivariable systems and incorporate constraints, is applied in order to compensate inaccuracies and measurement noise. The optimization problem is recast as a Quadratic Programming (QP) which is highly robust and efficient. Multi-step optimization is introduced to bring the unhealthy voltages as close as possible to the normal operating points so that leads to minimize the changes of the control variables. In order to regulate the power flow between the grid and the generator, it is essential to update reactive power with real power and actual terminal voltage besides reaching maximum reactive power. In this study, the updated control input applies feedback to MPC at each control step by solving a new optimization problem.
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