Low‐voltage ride‐through handling in wind farm with doubly fed induction generators based on variable‐step model predictive control

Nie, Yonghui; Zhang, Jie; Liu, Tianyu; Cui, Jiaxu; Zhang, Youmin

doi:10.1049/rpg2.12752

Cited by 3 publications

(3 citation statements)

References 34 publications

(45 reference statements)

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“…Voltage and rotor-speed instability could eventually result from this process, which is highly possible if the wind turbine is connected to a weak network [22]. Te following equation gives the mechanical power drawn from the wind by the rotor of the SCIG and the DFIG wind turbines [23,24]:…”

Section: Squirrel Cage Induction Generatorsmentioning

confidence: 99%

Investigation of the Impact of SSSC-Based FLC on the Stability of Power Systems Connected to Wind Farms

Abdollahi Chirani,

Karami

2024

International Transactions on Electrical Energy Systems

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The integration of renewable energy sources into power systems has increased significantly in recent years. Among various types of renewable energy, the use of wind energy is growing rapidly due to its low operating cost, wide distribution worldwide, and no greenhouse gas emissions. However, power systems integrated with wind energy may face stability and reliability issues due to the intermittent nature of wind power. Therefore, in power systems connected to wind farms, it is usually required to use some compensators such as static synchronous series compensator (SSSC) to increase the system performance under abnormal conditions. On the other hand, for an SSSC to be effective in improving the system performance, it must be equipped with a suitable controller. In this paper, a fuzzy logic controller (FLC) is used for the SSSC because of its advantages over conventional controllers. Extensive research has been conducted in power systems with wind turbines in which SSSC or FLC has been used; however, their simultaneous application in such systems has received less attention. Therefore, this article aims to fill this gap. The proposed method is implemented on two power systems and the simulation results are analyzed. In both systems, the dynamic behavior of three different wind farms is examined. In the first and second wind farms, either a squirrel cage induction generator (SCIG) or doubly-fed induction generator (DFIG) are used, whereas in the third one which is a combined wind farm (CWF), an equal number of SCIG and DFIG are employed. In wind farms with SCIG or DFIG, an SSSC is also utilized. Furthermore, an FLC is employed for the SSSC to improve its efficacy. A proportional integral (PI) controller is also considered for the SSSC, and its results are compared with FLC results. The simulation results confirm the superiority of FLC over PI controller.

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Section: Squirrel Cage Induction Generatorsmentioning

confidence: 99%

Investigation of the Impact of SSSC-Based FLC on the Stability of Power Systems Connected to Wind Farms

Abdollahi Chirani,

Karami

2024

International Transactions on Electrical Energy Systems

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“…Ignore the stator resistance loss and assume that the stator flux remains constant because the grid power supply supplies the stator with a constant voltage 5 . Simplifying the amplitude of the stator voltage we get:…”

Section: Fig 2 Flux and Voltage Vector Settingsmentioning

confidence: 99%

“…1 illustrates the detailed configuration that was used for this work. In this structure, the stator windings of DFIG are directly coupled to the three-phase electric grid, whereas the rotor windings are coupled to the main mover by an AC-DC-AC converter 5 . The AC-DC-AC converter provides three-phase rotor excitation power with modifiable frequency, phase, and magnitude and guarantees that the slip power can flow on both sides.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wind Energy Generation: Designing and Optimizing a Novel FO Fuzzy PD+I Regulator Using the SSO Method

Dembri,

Rahmani,

Babes

et al. 2024

Preprint

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In this article, an optimized design methodology is presented for a Fractional-Order Fuzzy Proportional-Derivative with Integral (FO Fuzzy PD + I) regulator using the Social Spider Optimization (SSO) technique. The research focuses on its application in the control of Doubly Fed Induction Generator (DFIG)-based wind turbine systems (WTS). The FO Fuzzy PD + I controller integrates the capabilities of the Fuzzy intelligent regulator and the Fractional-Order Proportional-Integral-Derivative (FO-PID) controller, enhancing DFIG current control while allowing independent control of active and reactive power. The approach is incorporated within the Direct Vector Control (DVC) strategy of the DFIG's rotor-side converter (RSC), replacing the conventional Proportional-Integral (PI) regulator in the internal current loops. Extensive performance evaluations are conducted under various operating conditions, including active power reference changes, parameter uncertainties, and rapid wind speed variations. Comparative analyses with SSO-optimized PID and Fuzzy regulators show the FO Fuzzy PD + I regulator performs better in terms of maximum overshoot, extreme undershoot, settling time, and Total Harmonic Distortion (THD) reduction. These findings underscore the regulator's potential for enhancing the reliability and efficiency of DFIG-based WTS. Furthermore, the adaptability of the FO Fuzzy PD + I regulator positions it as a versatile control solution with implications extending beyond the realm of wind energy, potentially benefiting various industrial sectors that demand precise and dynamic control mechanisms. As renewable energy sources continue to gain prominence in the global energy landscape, this research contributes to the broader mission of realizing a sustainable and eco-friendly energy future.

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Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties

Ma,

Wang,

Nian

et al. 2024

Applied Sciences

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Doubly fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input.

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Low‐voltage ride‐through handling in wind farm with doubly fed induction generators based on variable‐step model predictive control

Cited by 3 publications

References 34 publications

Investigation of the Impact of SSSC-Based FLC on the Stability of Power Systems Connected to Wind Farms

Investigation of the Impact of SSSC-Based FLC on the Stability of Power Systems Connected to Wind Farms

Enhancing Wind Energy Generation: Designing and Optimizing a Novel FO Fuzzy PD+I Regulator Using the SSO Method

Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties

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