Real-Time Simulator based hybrid control of DFIG-WES

Jaladi, Kiran Kumar; Sandhu, K. S.

doi:10.1016/j.isatra.2019.03.024

Cited by 22 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study [37] focused on the analysis and design of a hybrid control (HC) system for a doubly-fed asynchronous generator (DFIG) using a recurrent neural network (RNN) and a proportional-integral (PI) controller. The proposed hybrid controller exhibits fast dynamics and good transient response to sudden changes in wind speed and generator speed.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization of Rotor-Side PI Controller Parameters for Doubly-Fed Wind Turbines Based on Improved Recurrent Neural Networks Under Wind Speed Fluctuations

Cheng,

Wu,

Wang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

This paper investigates a doubly-fed wind turbine generation system (DFIG) where the rotorside control parameters have a significant impact on the effectiveness of the DFIG due to the adoption of its inner-loop current and outer-loop power control strategies. Under rated operation, the original DFIG parameter adjustment relies mainly on manual adjustment. In this paper, mathematical models are established through literature research and data search, and neural networks are found to have unique advantages in dynamic automatic parameter tuning. First, a mathematical model of DFIG based on PI controller is established in this paper, and then the improved recurrent neural network is applied to the parameter tuning control of rotor-side PI controller, and an experimental model of DFIG simulation based on the improved recurrent neural network is established in MATLAB/Simulink. By comparing the DFIG models before and after the improvement, the simulation experiments verify that the DFIG system based on the improved recurrent neural network (CLR-DRNN) has significant control advantages under the wind speed fluctuation. The simulation experimental results show that the DFIG system based on the improved recurrent neural network achieves significant improvement in wind energy utilization coefficient, active power, reactive power, response time of rotor speed, overshoot and static error compared with the conventional PI-regulated DFIG system.INDEX TERMS PI controller, double-fed wind turbine (DFIG), diagonal recurrent neural network (DRNN), wind power generation (WF).

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization of Rotor-Side PI Controller Parameters for Doubly-Fed Wind Turbines Based on Improved Recurrent Neural Networks Under Wind Speed Fluctuations

Cheng,

Wu,

Wang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In recent years, wind power has stood out among the commonly developed renewable energy sources as it is fresh, yields no harmful gasses, and abundantly available in nature. 1,2 The contribution of wind power is increasing day-by-day in the total installed capacity of energy globally. Nowadays, DFIG has been the most preferred machine for wind energy systems due to its capability of work in variable speed to extract maximum power, provides low cost, flexible control mechanism, and reduced power loss.…”

Section: Introductionmentioning

confidence: 99%

Disturbance rejection of the powers and DC ‐link voltage of a doubly‐fed induction generator using state‐space based linear quadratic integral optimal control approach

Bhushan

Kumar²,

Chatterjee

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

In this paper, an optimal linear quadratic regulator (LQR)-based integral controller (i.e., linear quadratic integral [LQI]) has been designed with an innovative approach for the tracking problems of a wind-driven doubly-fed induction generator (DFIG) system. The main objective of this controller is to reject the deviations that occurred in the DFIG powers together with the DC-link voltage due to the perturbations in the stator voltage. The purpose of the inclusion of integral action with the well-known modern optimal controller LQR is to guarantee zero steady-state error for disturbance rejection/set-point tracking. Further, the genetic algorithm (GA) technique is included for the optimal design of the LQI weighting matrices, which rapidly, as well as effectively rejects the disturbances from the system by regulating the augmented state variables. Therefore, to overcome these issues we have chosen the appropriate control model, which controls as a whole, that is, a multi-input-multi-output (MIMO) system. The DFIG system is a MIMO system, which has been modeled in a state-space approach in the d-q axes rotating frame of reference. To examine the effectiveness and robustness of the suggested control strategy, small-signal stability has been done by eigen values and participation factors method on digital simulations in the MATLAB/Simulink environment. Furthermore, the optimal control solutions have been tested under the various larger List of Symbols and Abbreviations: Δ, small deviation; ϕ s , angle between the voltage of slack bus and DFIG bus; ψ qr , ψ dr , d-q axes components of the rotor flux linkages; ψ qs , ψ ds , d-q axes components of the stator flux linkages; ω B , base speed; ω r , generator-angular speed; ω s , synchronous angular speed; C dc , capacitance of the DC-link; f, nominal frequency; H, combined inertia of wind turbine and generator; i dc , DC-link capacitor current; i qg , i dg , d-q axes components of the GSC current; i qr , i dr , d-q axes components of the RSC current; i qs , i ds , d-q axes components of the stator side current; i qt , i dt , d-q axes components of the total transmission line current; I t , total transmission line current; L m

show abstract

“…In Reference 37, real‐time modeling and simulation of complete closed loop control of DFIG for wind generation system is presented with OPAL‐RT digital real‐time simulator which is based on RT‐LAB platform with the models built‐in Simulink. A recurrent neural network and proportional Integral (PI) controller‐based hybrid control for DFIG is implemented under real‐time simulator‐based OPAL‐RT and MATLAB/Simulink in Reference 38.…”

Section: Introductionmentioning

confidence: 99%

Event analysis and real‐time validation of doubly fed induction generator‐based wind energy system with grid reactive power exchange under sub‐synchronous and super‐synchronous modes

Thomas

Asok

2020

Engineering Reports

View full text Add to dashboard Cite

The insertion of renewable power generators into the power system network has been promoted by the environment protection aspects. Doubly fed induction generator (DFIG)-based wind energy system is one of the most viable technologies for sustained power generation. This paper focuses on the operational analysis of DFIG-based wind energy conversion systems (WECS) with real-time experimental validation. An event analysis is executed under sub-synchronous and super-synchronous speeds with the variation in wind velocity and reactive power exchange by controlling the rotor side converter and grid side converter under MATLAB/Simulink platform. The results of the analysis with reactive power exchange can be utilized for the enhanced control of power electronic converters of DFIG-based WECS for better support to the grid. The validation of results is accomplished by emulation of 2 MW DFIG-based three blade wind turbine system in the laboratory under a real-time hardware-in-the-loop platform. K E Y W O R D S doubly fed induction generator, hardware-in-the-loop, renewable energy sources, renewable power generators, wind energy conversion systems This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Real-Time Simulator based hybrid control of DFIG-WES

Cited by 22 publications

References 19 publications

Dynamic Optimization of Rotor-Side PI Controller Parameters for Doubly-Fed Wind Turbines Based on Improved Recurrent Neural Networks Under Wind Speed Fluctuations

Dynamic Optimization of Rotor-Side PI Controller Parameters for Doubly-Fed Wind Turbines Based on Improved Recurrent Neural Networks Under Wind Speed Fluctuations

Disturbance rejection of the powers and DC ‐link voltage of a doubly‐fed induction generator using state‐space based linear quadratic integral optimal control approach

Event analysis and real‐time validation of doubly fed induction generator‐based wind energy system with grid reactive power exchange under sub‐synchronous and super‐synchronous modes

Contact Info

Product

Resources

About