Developing precise and robust algorithms that can help in obtaining maximum power yield in a variable speed wind turbine is an important area of research in wind engineering. The present manuscript proposes a technique that utilizes a second-generation CRONE controller for the maximum power tracking technique (MPPT) to maximize power generation in a wind energy conversion system (WECS) based on a double-fed induction generator (DFIG). The authors propose this novel method because the classical controllers cannot provide adequate performance in terms of extracting the maximum energy from variable speed wind turbines when applying a real wind profile and they cannot guarantee the high stability of the WECS. Moreover, this novel controller sufficiently handles problems related to the control effort level. The performance of the second-generation CRONE method was mathematically modeled using MATLAB/Simulink and compared with four other types of MPPT control techniques, which include a proportional-integral linear controller (PI), nonlinear sliding mode controller (SMC), backstepping controller (BS), and fuzzy logic controller (FLC). Two different wind profiles, a step wind profile and a real wind profile, were considered for the comparative study. The response time, dynamic error percentage, and static error percentage were the quantitative parameters compared, and the qualitative parameters included set-point tracking and precision. This test demonstrated the superiority of the second-generation CRONE controller in terms of all of the compared parameters.
In this research paper, a nonlinear Backstepping controller has been proposed in order to improve the dynamic performance of a doubly fed induction generator (DFIG) based Wind Energy conversion System, connected to the grid through a back-to-back converter. Firstly, an overall modeling of proposed system has been presented. Thereafter, three control techniques namely backstepping (BSC), sliding mode (SMC) and field-oriented control (FOC) using a conventional PI regulator have been designed in order to control the stator active and reactive powers of the DFIG. In addition, the maximum power point tracking (MPPT) strategy has been investigated in this work with three mechanical speed controllers: BSC, SMC and PI controller with the aim of making a synthesis and a comparison between their performances to determine which of those three techniques is more efficient to extract the maximum power. Finally, a thorough comparison between the adopted techniques for the DFIG control has been established in terms of response time, rise time, total harmonic distortion THD (%) of the stator current, static errors and robustness. The effectiveness and robustness of each control approach has been implemented and tested under MATLAB/Simulink environment by using a 1.5 MW wind system model.
The integration of wind energy systems into the electric grid has become inevitable despite the many problems associated with this integration. Most of these problems are due to variations in wind speed. The problems are for example oscillations in the power generated, which implies the lack of guarantee of obtaining the maximum energy and the ripple in the electromechanical torque of the generator. This work aims at mitigating these problems for wind energy conversion system-driven doubly-fed induction generator (DFIG), which is the main wind turbine utilized for energy applications. This mitigation is performed through direct reactive and active powers control of the DFIG using an artificial neural network. A DSP (Digital Signal Processor-dSPACE DS1104) was used to experimentally test the proposed strategy. The dynamic performances of the controlled generator are analyzed by using the designed intelligent control strategy in the case of variable wind speeds and upon sudden change of the active power demand. Based on the obtained experimental results, it can be said that the designed intelligent control strategy outperforms traditional methods like direct power (DPC) and vector control in terms of reducing the current harmonics, and torque ripples, and enhancing dynamic response.
Direct power control (DPC) is among the most popular control schemes used in renewable energy because of its many advantages such as simplicity, ease of execution, and speed of response compared to other controls. However, this method is characterized by defects and problems that limit its use, such as a large number of ripples at the levels of torque and active power, and a decrease in the quality of the power as a result of using the hysteresis controller to regulate the capacities. In this paper, a new idea of DPC using artificial neural networks (ANNs) is proposed to overcome these problems and defects, in which the proposed DPC of the doubly fed induction generators (DFIGs) is experimentally verified. ANN algorithms were used to compensate the hysteresis controller and switching table, whereby the results obtained from the proposed intelligent DPC technique are compared with both the classical DPC strategy and backstepping control. A comparison is made between the three proposed controls in terms of ripple ratio, durability, response time, current quality, and reference tracking, using several different tests. The experimental and simulation results extracted from dSPACE DS1104 Controller card Real-Time Interface (RTI) and Matlab/Simulink environment, respectively, have proven the robustness and the effectiveness of the designed intelligence DPC of the DFIG compared to traditional and backstepping controls in terms of the harmonic distortion of the stator current, dynamic response, precision, reference tracking ability, power ripples, robustness, overshoot, and stability.
Wind speed variations affect the performance of the wind energy conversion systems (WECSs) negatively. This paper addressed an advanced law of the backstepping controller (ABC) for enhancing the integration of doubly fed induction generator (DFIG)-based grid-connected WECS under wind range of wind speed. This enhancement was achieved through three control schemes, which were blade pitch control, rotor-side control, and grid-side control. The blade pitch control was presented to adjust the wind turbine speed when the wind speed exceeds its rated value. In addition, the rotor and grid-side converter controllers were presented for improving the direct current link voltage profile and achieving maximum power point tracking (MPPT) under speed variations, respectively. To evaluate the effectiveness of the proposed ABC control, a comparison between PI and sliding-mode control (SMC) was presented, considering the parameters of a 1.5 MW DFIG wind turbine in the Assilah zone in Morocco. Moreover, some changes in the DFIG parameters were introduced to investigate the robustness of the proposed controller under parameter uncertainties. Simulation results showed the capability of the proposed ABC controller to enhance the performance of the DFIG-WECS based on variable speed and variable pitch turbine, at both below and above-rated speed, leading to an error around 10−3 (p.u), with an ATE = 0.4194 in the partial load region; in terms of blade pitch control, an error of 2.10−4 (p.u) was obtained, and the DC-link voltage profile showed a measured performance of 5 V and remarkable THD value reduction compared to other techniques, with a measured THD value of 2.03%, 1.67%, and 1.46% respectively, in hyposynchronous, hypersynchronous, and pitch activation modes of operation. All simulations were performed using MATLAB/SIMULINK based on real wind profiles in order to make an exhaustive analysis with realistic operating conditions and parameters.
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