Evaluating the performances of PI controller (2DOF) under linear and nonlinear operations of DFIG-based WECS: A simulation study

Desalegn, Belachew; Gebeyehu, Desta; Tamrat, Bimrew

doi:10.1016/j.heliyon.2022.e11912

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…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].…”

Section: A Literature Reviewmentioning

confidence: 99%

Analysis of Wind Characteristics for Grid-Tied Wind Turbine Generator Using Incremental Generative Adversarial Network Model

Behara,

Saha

2024

IEEE Access

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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 This article has been accepted for publication in IEEE Access.

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Section: A Literature Reviewmentioning

confidence: 99%

Analysis of Wind Characteristics for Grid-Tied Wind Turbine Generator Using Incremental Generative Adversarial Network Model

Behara,

Saha

2024

IEEE Access

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear robust sliding mode - Backstepping hybrid control for WECS -theoretical design and experimental evaluation

Echiheb,

Elkafazi,

Bossoufi

et al. 2024

Heliyon

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“…In DFIG-WECS, the traditional proportional integral (PI) [22,23] controller has been frequently used. The primary challenge with PI controllers is correctly adjusting their parameters, which is a stimulating nonlinear problem [24]. Even with all of the aforementioned advantages, a conventional PI controller will not function as intended if the variables are not chosen appropriately.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

2023

IJATEE

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Power flow optimization is a critical aspect of power and control system design, aiming to minimize running costs while ensuring control variable tolerances and maximizing power delivery. The utilization of flexible alternating current transmission system (FACTS) particularly unified power flow controllers (UPFCs), enhances a system's power transfer capacity. UPFCs offer versatile adjustments for active and reactive power lines and voltage levels simultaneously, contributing to improved system performance and stability. A novel approach for enhancing flexibility and control in a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) was proposed. The technique involved the integration of a fuzzy-based control strategy for UPFC and the utilization of a cascaded adaptive neuro fuzzy inference system (ANFIS) to manage UPFC's series and shunt converters. Additionally, an improved butterfly optimization algorithm (IBOA) was introduced to ensure stability for DFIG-WECS. The methods were implemented and validated through simulations using MATLAB software. The effectiveness of the proposed approach was demonstrated through experimental results. The integration of cascaded ANFIS-based UPFC control and IBOA with WECS resulted in increased system power quality and stability. The total harmonic distortion (THD) of the system is reduced by 1.8%, indicating an enhancement in power quality. The application of UPFC and innovative control methodologies results in optimal power flow (OPF), maintaining stringent control variable tolerances in power systems. This research presents a systematic approach to enhancing flexibility and control in a DFIG-based WECS through the innovative use of UPFC and advanced control techniques. The proposed fuzzy-based UPFC control, in conjunction IBOA, significantly improves power quality and stability, thereby enhancing the overall efficiency of the system.

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Evaluating the performances of PI controller (2DOF) under linear and nonlinear operations of DFIG-based WECS: A simulation study

Cited by 7 publications

References 41 publications

Analysis of Wind Characteristics for Grid-Tied Wind Turbine Generator Using Incremental Generative Adversarial Network Model

Analysis of Wind Characteristics for Grid-Tied Wind Turbine Generator Using Incremental Generative Adversarial Network Model

Nonlinear robust sliding mode - Backstepping hybrid control for WECS -theoretical design and experimental evaluation

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

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