This paper presents an experimental implementation of a Neural Sliding Mode Linearization approach for the control of a double-fed induction generator connected to an infinite bus via transmission lines. The rotor windings are connected to the grid via a back-to-back converter, while the stator windings are directly coupled to the network. The chosen control scheme is applied to obtain the required stator power trajectories by controlling the rotor currents and to track the desired values of the DC-link output voltage and the grid power factor. This controller is based on a neural identifier trained online using an Extended Kalman Filter. Based on such identifier, an adequate model is obtained, which is used for synthesizing the required controllers. The proposed control scheme is experimentally verified on 1/4 HP DFIG prototype considering normal and abnormal grid conditions. In addition, maximum power extraction from a random wind profile is tested in the presence of different grid scenarios. Moreover, a comparison with conventional control schemes is performed. The obtained results illustrate the capability of the proposed control scheme to achieve active power, reactive power, and DC voltage desired trajectories tracking and to operate the wind power system even in the presence of parameter variation and grid disturbances, which helps to ensure the stability of the system and improve generated power quality.
This paper presents a comparative study between the conventional PI (Proportional Integral) and backstepping controllers applied to the DFIG (Doubly Fed Induction Generator) used in WECS (Wind Energy Conversion System). These two different control strategies proposed in this work are developed to control the active and reactive power of the DFIG on the one hand, and to maintain the DC-link voltage constant for the inverting function on the other hand. This is ensured by generating control signals for two power electronic converters, RSC (Rotor Side Converter) and GSC (Grid Side Converter). In order to optimise the power production in the WT (Wind Turbine), an MPPT (Maximum Power Point Tracking) algorithm is applied along with each control technique. To simulate the effectiveness of the proposed controllers, MATLAB/Simulink Software is used, and the obtained results are analysed and discussed to compare PI and backstepping controllers in terms of robustness against wind speed variations and tracking performance in dynamic and steady states.
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