Small signal stability enhancement of DFIG based wind power system using optimized controllers parameters

Mehta, Bhinal; Bhatt, Praghnesh; Pandya, Vivek

doi:10.1016/j.ijepes.2015.01.039

Cited by 43 publications

(20 citation statements)

References 14 publications

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“…The damping controller model consists of the washout block, gain block and cascaded phase compensation block. The rotor speed deviation Δω is the controller input and output is the damping control signal (ΔU E ) given to generator-excitation system feedback loop [17] [18].…”

Section: Controllers For Dfigmentioning

confidence: 99%

Small Signal Stability Analysis of DFIG Fed Wind Power System Using a Robust Fuzzy Logic Controller

Selvam¹,

Periasamy²

2016

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This paper focuses on the small signal stability analysis of Doubly-Fed Induction Generator (DFIG) fed wind power system under three modes of operation. The system stability is affected by the influence of electromechanical oscillations, which can be damped using Power System Stabilizer (PSS). A detailed modeling of DFIG fed wind system including controller has been carried out. The damping controller is designed using fuzzy logic to damp the oscillatory modes for stability. The robust performance of the system with controllers has been evaluated using eigen value analysis and time domain simulations under various disturbances and wind speeds. The effectiveness of the proposed fuzzy based PSS is compared with the performance of conventional PSS implemented in the wind system.

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Section: Controllers For Dfigmentioning

confidence: 99%

Small Signal Stability Analysis of DFIG Fed Wind Power System Using a Robust Fuzzy Logic Controller

Selvam¹,

Periasamy²

2016

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“…For a particular rotor speed ω r , the reference torque T ref for maximum power extraction can be obtained from wind turbine characteristics. With the computed value of T ref , a reference rotor current in q axis I qr_ref can be obtained as given by equation (12) (Mehta et al,2015). …”

Section: Controllersmentioning

confidence: 99%

“…The terminal voltage (V) is compared with the reference voltage and the reference current (Idr_ref) get adjusted appropriately. The input to the PI controller is the difference between Idr_ref and I dr and the output is the required d-axis rotor voltage V dr (Mehta et al, 2015). V/(x m ω)…”

Section: Controllersmentioning

confidence: 99%

“…Mehta et al (2014) analyzed the impact of penetration of DFIG-based wind power generation on oscillatory stability of two area interconnected power system and found that the oscillatory instability can be stabilized with the coordinated operation of automatic voltage regulator (AVR) and power system stabilizer (PSS) equipped on synchronous generators. Mehta et al (2015) found that the optimization of controller parameters of a DFIG-based wind generation system employing particle swarm optimization technique minimized the oscillations in rotor currents and electromagnetic torque. Chatterjee et al (2016) in their work used teaching learning based optimization (TLBO) algorithm to optimize the gains of PI controllers associated with DFIG and showed that the TLBO provides superior end results than PSO.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of small signal stability of a DFIG-based wind power system using fuzzy logic control

Renuka¹,

Reji²,

Sreedharan³

2016

Int. J. Eng. Sci. Tech

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This paper proposes fuzzy logic controllers for enhancing the small signal stability of DFIG-based wind integrated power system. The test system used is single machine infinite bus system integrated with conventional proportional-integral controllers. The fuzzy logic controllers provide optimum proportional and integral gains under various operating conditions namely wind speed and grid strength. The effects of strong and weak grid strengths have been taken into account with short circuit level of 40 MVA and 16 MVA, respectively. The obtained result justifies that the damping ratio and there by the small signal stability of such a system have been enhanced considerably by the action of fuzzy logic controllers. The generalization can be enlarged to multi-machine systems in various dynamic conditions.

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“…In the literature, the basic concepts of the mathematical modeling of the DFIG system and its state‐space linearization methods have been proposed. Because of simplicity, the affluence of accomplishing and the robustness of the proportional integral (PI) controller have made it to continue to be the most popular controllers over the last three decades.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and control of DFIG-based wind energy system by using optimized linear quadratic regulator weight matrices

Bhushan

Chatterjee

2017

Int Trans Electr Energ Syst

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Summary This paper deals with a genetic algorithm (GA)–based linear quadratic regulator (LQR) controller to improve the dynamic response, stability, and robustness of the doubly fed induction generator (DFIG) system at various stator voltage disturbances. The complete model has been represented by a state‐space model. This helps to optimally control all the states through the full‐state feedback LQR controller. GA is employed in the LQR algorithm for optimal tuning of the Q and R matrices. For finding out the effectiveness of the proposed controller, its dynamic response has been compared with proportional integral (PI) and LQR controllers. The simulation results indicate that the proposed controller has better performance in comparison to the PI and LQR controllers in terms of peak value, settling time, rise time, and steady‐state values of the DFIG outputs. The stability and robustness of the study system have also been investigated by the eigenvalue and participation factor analyses.

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Small signal stability enhancement of DFIG based wind power system using optimized controllers parameters

Cited by 43 publications

References 14 publications

Small Signal Stability Analysis of DFIG Fed Wind Power System Using a Robust Fuzzy Logic Controller

Small Signal Stability Analysis of DFIG Fed Wind Power System Using a Robust Fuzzy Logic Controller

Enhancement of small signal stability of a DFIG-based wind power system using fuzzy logic control

Mathematical modeling and control of DFIG-based wind energy system by using optimized linear quadratic regulator weight matrices

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