Abstract
This paper aims at contributing to the modeling and control of a variable speed Wind Energy Conversion System (WEC‐System) based on a Squirrel Cage Induction Generator (SCI‐Generator). The connection between the SCI‐Generator and the main utility grid is achieved by back‐to‐back three phase power converters (Generator and Grid Side Converters). A new control strategy named the Active Disturbance Rejection Control (ADRC) is proposed and utilized to control the Wind Energy Conversion (WEC) system based on the SCI‐Generator. The objective is to control both the generator and the grid side converters in order to operate the system and to ensure the connection with the power grid. The first converter is used to control the SCI‐Generator speed and field to extract the available maximum power from the wind turbine by using a Maximum Power Point Tracking (MPPT) technique and, also, to ensure that the extracted power does not exceed its rated value in case of strong wind speeds; in this case a pitch actuator system is used to control the blades pitch angle of the wind turbine. The second converter is used to control the active and reactive powers injected into the utility grid as well as to regulate the DC‐Link Voltage. This control takes into account the rejection of internal disturbances as the variation of electrical parameters (the resistance, the inductance…) and the external disturbances as voltage dips and frequency droops in the main grid. To test and validate the performances of the proposed controller, a series of simulations were developed under MATLAB/Simulink environment, and the results have demonstrated the effectiveness of the proposed control under different case of simulations.
Highlights
Very early in the history of technology, wind was exploited to extract mechanical energy from it, where the conversion of wind energy into mechanical energy is relatively easy; it is only necessary to have a satisfactory potential and to resist the whims of excessive winds
Several techniques were used going from the utilization of classical extreme algorithms such as Perturb and Observes (P&O), Incremental Conductance (INC), and Hil Climbing (HCS) [1] to those based on artificial intelligence as Fuzzy Logic and Neural networks [2]
This is why the literature commonly refers to Maximum Power Point Tracking (MPPT) algorithms that seek to bring the operating point to the maximum point that this function forms; we can find among them the tip speed ratio (TSR) and Optimal Torque Control (OTC) techniques; the last one is to be used in this paper
Summary
Very early in the history of technology, wind was exploited to extract mechanical energy from it, where the conversion of wind energy into mechanical energy is relatively easy; it is only necessary to have a satisfactory potential and to resist the whims of excessive winds. The proliferation of wind turbines has led electrical engineering researchers to conduct investigations to improve the efficiency of electromechanical conversion and the quality of the energy supplied. For better operation of these systems, Maximum Power Point Tracking (MPPT) algorithms are Mathematical Problems in Engineering needed to improve the energy efficiency and to extract the maximum power available from the wind turbine. The wind turbine has an advantages that it can be reliably characterized by its specific function Cp(λ) This is why the literature commonly refers to MPPT algorithms that seek to bring the operating point to the maximum point that this function forms; we can find among them the tip speed ratio (TSR) and Optimal Torque Control (OTC) techniques; the last one is to be used in this paper
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