Robust Control Schemes for a Doubly Fed Induction Generator based Wind Energy Conversion System

Abstract

Due to many advantages, such as variable speed operation, low noise, high torque, and ease of maintenance, the Doubly Fed Induction Generator (DFIG) is extensively employed in a Wind Energy Conversion System (WECS). To control a DFIG-based WECS, active power extraction from the wind must be regulated while reactive power must be kept at zero in order to ensure unity power factor operation. A number of control algorithms for controlling the active and reactive power of WECS have been proposed in the past. A WECS is encountered with several parametric uncertainties and external disturbances. Thus, it is essential to tackle the impact of parametric uncertainties and disturbances in the WECS characteristics by suitable design and implementation of appropriate robust control algorithms to achieve good performance. Controlling the active and reactive power of WECS has been the subject of a lot of research. Parametric uncertainties, on the other hand, have a substantial impact on the performance of active and reactive power regulation in a WECS. As a result, developing appropriate controllers for managing the active and reactive power of the WECS in the presence of parametric uncertainties and disturbances is regarded as a challenging control problem. The purpose of this dissertation is to develop robust control algorithms for a DFIG-based grid-connected WECS that can control both active and reactive power in the presence of parametric uncertainties and disturbances. As the active and reactive power of a DFIG are dependent, it becomes necessary to design suitable controller such as that the active and reactive power can be regulated separately by decoupling the active and reactive power control loops. The thesis starts with the development of a Proportional-Integral (PI) controller and Sliding Mode Controller (SMC) to control the active and reactive power of DFIG-based WECS that are delivered to the grid. The performance of PI and SMC controllers are evaluated under nominal conditions. An Autoregressive Moving Average with Exogenous (ARMAX) model is developed for DFIG-based WECS. This ARMAX model of the WECS is used to design a Model Predictive Control (MPC). By using the input-output values from previous sampling instants over a time horizon, the MPC predicts the system’s future output. The computing time, on the other hand, is a substantial barrier to MPC implementation. As a result, a variety of approaches have been used to lessen MPC’s computational load. To lessen the time complexity of the MPC problem, usually optimal solutions are adopted. A Linear Matrix Inequality (LMI) approach is used to reduce the computation time caused by the MPC. The optimization problem is solved using the LMI. PI is an excellent choice in the majority of industrial applications among all classical and current control methods. Because WECS has uncertainties due to intermittency in the wind speed various suitable feedback control mechanisms are necessary to address these concerns. An Extended State Observer (ESO) successfully estimates the unknown dynamics and disturbance. The rotor resistance and mutual inductance of the DFIG are modified to evaluate the robustness of the proposed MADRC. Peak overshoot and settling duration of the active power response are studied as a function of the aforementioned parameters. In the face of parametric uncertainty, the developed controller is found effective in managing the active and reactive power of DFIG, as well as rejecting external disturbance in desired value tracking. The proposed MADRC successfully handles parametric variation for set point tracking of active and reactive power of WECS, according to simulation and experimentation results. To improve the tracking accuracy of a DFIG-based WECS an online optimization approach based on wavelet neural networks for parameter adjustment of Active Disturbance Rejection Control (ADRC).