Sensorless control of doubly-fed induction generators in variable-speed wind turbine systems

Abdelrahem, Mohamed; Hackl, Christoph M.; Kennel, Ralph

doi:10.1109/iccep.2015.7177656

Cited by 47 publications

(25 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A variety of speed and position estimation methods have been proposed for permanent-magnet synchronous machines (PMSMs) and induction machines (IMs), and recently they were applied successfully to SMPMSGs/DFIGs. The well-known observers in the literature include the following: phase-locked loop (PLL) [13][14][15], model reference adaptive system (MRAS) [16][17][18][19][20][21][22], sliding-mode observers [23][24][25], extended Kalman filter (EKF) [26][27][28], unscented Kalman filter (UKF) [29][30][31], and others. Due to the simplicity, ease of implementation, and direct physical interpretation, MRAS-based observers have received increased interest from researchers and engineers.…”

Section: Introductionmentioning

confidence: 99%

Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems

et al. 2020

Self Cite

View full text Add to dashboard Cite

In micro-grid systems, wind turbines are essential power generation sources. The direct-driven surface-mounted permanent-magnet synchronous generators (SMPMSGs) in variable-speed wind generation systems (VS-WGSs) are promising due to their high efficiency/power density and the avoidance of using a gearbox, i.e., regular maintenance and noise are averted. Usually, the main goal of the control system for SMPMSGs is to extract the maximum available power from the wind turbine. To do so, the rotor position/speed of the SMPMSG must be known. Those signals are obtained by the help of an incremental encoder or speed transducer. However, the system reliability is remarkably reduced due to the high failure rate of these mechanical sensors. To avoid this problem, this paper presents a model reference adaptive system with finite-set (MRAS-FS) observer for encoderless control of SMPMSGs in VS-WGSs. The motif of the presented MRAS-FS observer is taken from the direct-model predictive control (DMPC) principle, where a certain number of rotor position angles are utilized to estimate the stator flux of the SMPMSG. Subsequently, a new optimization criterion (also called quality or cost function) is formulated to select the best rotor position angle based on minimizing the error between the estimated and reference value of the stator flux. Accordingly, the traditional fixed-gain proportional-integral regulator generally employed in the classical MRAS observers is not needed. The proposed MRAS-FS observer is validated experimentally, and its estimation response has been compared with the conventional MRAS observer under different conditions. In addition to that, the robustness of the MRAS-FS observer is tested at mismatches in the parameters of the SMPMSG.

show abstract

Section: Introductionmentioning

confidence: 99%

Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Of these, [6][7][8][9][10][11][12][13][14][15][16][17] are related to the different control applications of DFIM. Kalman-filter-based speed estimation methods as reported in [6,[18][19][20] are robust to machine parameter variation but are computationally intensive. Various full-and reduced-order observer-based speed sensorless methods are reported in [7,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

MRAS‐based speed estimation of grid‐connected doubly fed induction machine drive

Kumar

Das

2017

IET Power Electronics

View full text Add to dashboard Cite

This study presents a new model reference adaptive system (MRAS)-based speed estimation strategy for a gridconnected doubly fed induction machine (DFIM) drive. The reference and the adaptive models of the proposed MRAS utilise instantaneous and steady-state values, respectively, of a fictitious quantity obtained as the difference between two fictitious ratios of rotor voltage to current along the d and q axes. The dimension of this fictitious quantity is ohm (Ω) and has no physical significance. The proposed formulation is free from flux estimation and independent of stator and rotor resistance variations. This new formulation shows stable performance in the regenerating mode of operation of DFIM. All the relevant studies, in this context, are done in MATLAB/Simulink. The simulation study is further validated with a dSPACE-1104-based DFIM laboratory prototype. ⌢ sl actual, estimated slip speed (rad/s) ω r , ω ⌢ r actual, estimated rotor speed (rad/s) ω ms speed of stator flux (rad/s) R r , R s rotor, stator resistances (Ω) L r , L s , L m rotor self-, stator self-, mutual inductances (H) σ = 1 − L m 2 / L s L r machine leakage factor σL s , σL r stator and rotor transient inductance (H) p 'd/dt' operator , *, superscript s estimated, reference and stationary quantities θ ms , θ sl stator flux position and slip angle (elect. rad)

show abstract

“…[12], [13]. In [14] the behavior of two estimation techniques based on the Kalman filter is analyzed.…”

Section: Introductionmentioning

confidence: 99%

Application of extended Kalman filter to parameter estimation of doubly-fed induction generators in variable-speed wind turbine systems

Abdelrahem¹,

Hackl²,

Kennel³

2015

2015 International Conference on Clean Electrical Power (ICCEP)

Self Cite

View full text Add to dashboard Cite

This paper proposes a parameter estimation method for doubly-fed induction generators (DFIGs) in variable-speed wind turbine systems (WTS). The proposed method employs an extended Kalman filter (EKF) for estimation of all electrical parameters of the DFIG, i.e., the stator and rotor resistances, the leakage inductances of stator and rotor, and the mutual inductance. The nonlinear state space model of the DFIG is derived and the design procedure of the EKF is described. The observability matrix of the linearized DFIG model is computed and the observability is checked online for different operation conditions. The estimation performance of the EKF is illustrated by simulation results. The estimated parameters are plotted against their actual values. The estimation performance of the EKF is also tested under variations of the DFIG parameters to investigate the estimation accuracy for changing parameters.

show abstract

Sensorless control of doubly-fed induction generators in variable-speed wind turbine systems

Cited by 47 publications

References 30 publications

Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems

Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems

MRAS‐based speed estimation of grid‐connected doubly fed induction machine drive

Application of extended Kalman filter to parameter estimation of doubly-fed induction generators in variable-speed wind turbine systems

Contact Info

Product

Resources

About