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Owing to different stochastic characteristics of wind energy systems, there would commonly be uncertainties in the processes of wind energy conversion that may ultimately cause to severely degrade the quality of electric power production. These uncertainties include time-varying fluctuations of mechanical & electrical parameters that can be generat...
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... control model for a DFIG WECS of 2 MW rated-power is simulated in the MATLAB-SIMULINK software interface based on the wind speed of 10 m/s, and by making use of different built-in blocks along with the consideration of the system's manufacturer specifications that are presented under Appendix, in Table 5. This control model consists of different subsystems including electrical system design (Figure 11), aerodynamic system simulation (Figure 12), the wind speed model simulation (Figure 13), control system design (Figure 14), and PI controller (2DOF) design (Figure 15). The electrical design model is mainly built by using three-phase programmable voltage source, threephase V-I measurement, asynchronous machine, and DC voltage source-based universal bridge as SIMULINK blocks. ...
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The rapid increase in scale and sophistication of offshore wind (OSW) farms poses a critical challenge related to the cost-effective operation and management of wind energy assets. A defining characteristic of this challenge is the economic trade-off between two concomitant processes: power production (the primary driver of short-term revenues), an...
Citations
... However, the major drawback of this control method is the high sensitivity of the control system to the parameters of the system under study and the need to adjust the coefficients of the PI controller. For [32], they propose the improved control method IFOC. This strategy controls the electrical parameters used to implement the regulation of the rotor current and electromagnetic torque components in a DFIG WECS. ...
... The principle of these approaches is to independently control the active power and reactive power through stator current components, which makes the PMSG similar to the DC machine. However, the use of Proportional-Integral (PI) controllers makes the system performance dependent on precise machine parameters such as fixed resistance and mutual inductance, resulting in control sensitivity to parameter changes (Desalegn et al., 2022b). Alternative approaches to vector control have been proposed for PMSG-based wind turbine systems. ...
... To simplify the modeling of electrical machines, various assumptions should be considered while developing a comprehensive representation (Desalegn et al., 2022b;Mahfoud et al., 2022b): ...
... The design aspects and building of a DFIG are described in detail using AC/DC/AC PWM voltage source electronic regulators in the rotor side circuit and vector control used to both the GSC and the RSC without taking any abnormal condition into account [34], [35], [36], [37]. The comparison of proportional integral control and proportional distribution control for the regulation of reactive power in a wind farm is the exclusive focus of [38], [39], which concludes that proportional integral is more resilient than proportional distribution control. While a supervisory system manages the active and reactive power of the entire wind generation system, a machine-learning control scheme confirms that set points at the level of the wind turbines are reached [40]. ...
Wind attribute analysis is a crucial aspect of meteorological and environmental research, with applications ranging from renewable energy generation to weather forecasting. However, existing models encounter several challenges in accurately and comprehensively characterizing wind positions. In this context, the proposed Incremental Tuned Generative Adversarial Network model (incremental GAN model), based on an unsupervised learning approach, introduces innovative solutions to overcome these challenges and enhance the precision and reliability of wind position analysis. This research aims to enhance the reliability and efficiency of wind energy generation by analyzing wind conditions and providing accurate data for decision-making. It introduces an Incremental GAN that refines parameters based on various factors. This GAN model learns and predicts these parameters over time, improving its performance. It incorporates advanced techniques like a 2-level fused discriminator and self-attention for precise predictions of wind characteristics. The GAN model generates important parameters such as droop gain, which influences generator output in response to load or generation changes, aiding grid stability. It also optimizes the frequency control of different types of generators in the presence of wind farms. The model continuously monitors wind farm conditions, adjusting power injection into the grid as needed for efficient and reliable wind energy utilization.
... In previous studies on rotor side vector control of DFIGs, researchers have investigated various aspects of the control strategy, including: 1) Control algorithms: Several control algorithms have been proposed and analyzed for implementing rotor side vector control. These algorithms include proportional-integral (PI) controllers [6], proportional-resonant (PR) controllers [7] , and sliding mode controllers [8], among others. Researchers have compared their performance, stability, and robustness to disturbances and parameter uncertainties. ...
This paper focuses on the investigation of the performance of rotor side vector control of Doubly-Fed Induction Generators (DFIGs) used in wind turbines. These are widely utilised due to their low cost, high efficiency, and reliability. However, the dynamic behavior of DFIGs under varying wind speeds and grid conditions poses significant challenges in terms of stability. To address these issues, rotor side vector control is commonly employed. In this study, we present a review of existing research on rotor side vector control of DFIGs and provide an overview of the basic principles of DFIG operation and vector control concepts. The study analyzes the impact of various control parameters on the system performance under different wind speeds. The results suggest that rotor side vector control can improve the performance of DFIGs, and optimal control parameters depend on specific operating conditions. This study provides insights for the design, control, and optimization of DFIG-based wind energy conversion systems.
... The principle of these approaches is to independently control the active power and reactive power through stator current components, which makes the PMSG similar to the DC machine. However, the use of Proportional-Integral (PI) controllers makes the system performance dependent on precise machine parameters such as fixed resistance and mutual inductance, resulting in control sensitivity to parameter changes (Desalegn et al., 2022b). Alternative approaches to vector control have been proposed for PMSG-based wind turbine systems. ...
... To simplify the modeling of electrical machines, various assumptions should be considered while developing a comprehensive representation (Desalegn et al., 2022b;Mahfoud et al., 2022b): ...
In order to sustain the overall competitiveness of the wind power industry, unrelenting focus is required on working toward the advancement of enabling technologies and research studies that are associated with wind farm systems. First, wind farm technologies that include various turbine generator systems coupled with different power transmission configurations have enormous impact in determining the quality of wind power production. In addition, modern wind farms are expected to implement robust power control algorithms to meet more advanced requirements of electricity generation. Accordingly, this study explores the statuses of wind energy harvesting technologies and wind farm control strategies by discussing their recent and future impact on transforming the wind power industry. Doubly fed induction generator (DFIG)-based wind energy harvesting technology is well-matured and has exhibited an excellent track-record in past and recent experiences, but its capability of being further scalable for large-scale power production is limited as it is largely incompatible with high-voltage power transmission networks. On the other hand, permanent magnet synchronous generator (PMSG)-based technology is making significant advancements to attain the maximum possible efficiency level in greatly facilitating larger scale power generation, although the construction of bulky and costly power transmission systems is required. In this regard, future technological advances in the wind farm industry are expected to reasonably optimize the design and cost of high-voltage power transmission systems. Similarly, an increasing number of research studies are introducing a number of power optimization-based control models to create an ideal integration of the aforementioned wind farm technologies so as to ultimately enhance the reliability of electricity production by maintaining the systems’ safety. Yet, additional work is still expected to be undertaken in the future for a more extended evaluation of the performances of many different control models under a similar environment.
