Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor

Alonge, F.; Cangemi, Tommaso; D’Ippolito, Filippo; Fagiolini, Adriano; Sferlazza, Antonino

doi:10.1109/tie.2014.2355133

Cited by 118 publications

(47 citation statements)

References 23 publications

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“…This is useful for both control and diagnosis of the process [24]. Analysis on observability on 6 th order discrete time model based on 6 th order discrete time Extended Kalman Filter discusses the convergence of speed during transient conditions [25].…”

Section: Introductionmentioning

confidence: 99%

A Robust EKF Based Speed Estimator and Fuzzy Optimization Technique for Sensorless Induction Motor Drives

Lakshmi¹,

Suresh²

2017

IJPEDS

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<p>The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.</p>

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Section: Introductionmentioning

confidence: 99%

A Robust EKF Based Speed Estimator and Fuzzy Optimization Technique for Sensorless Induction Motor Drives

Lakshmi¹,

Suresh²

2017

IJPEDS

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“…Additionally, a braided EKF was proposed for sensorless control of IMs in [28], and challenging parameter and load variations in a wide speed range were considered to verify the algorithm. The convergence analysis of EKF for sensorless control of IM was presented in [29]. In spite of a number of research works and applications of EKF algorithms in IMs, there is a rare literature focusing on speed and position sensorless control of DFIGs by using EKF.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Control of Late-Stage Offshore DFIG-WT with FSTP Converters by Using EKF to Ride through Hybrid Faults

et al. 2017

Energies

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Abstract:A hybrid fault scenario in a late-stage offshore doubly-fed induction generator (DFIG)-based wind turbine (DFIG-WT) with converter open-circuit fault and position sensor failure is investigated in this paper. An extended Kalman filter (EKF)-based sensorless control strategy is utilized to eliminate the encoder. Based on the detailed analysis of the seventh-order dynamic state space model of DFIG, along with the input voltage signals and measured current signals, the EKF algorithm for DFIG is designed to estimate the rotor speed and position. In addition, the bridge arm open circuit in the back-to-back (BTB) power converter of DFIG is taken as a commonly-encountered fault due to the fragility of semiconductor switches. Four-switch three-phase (FSTP) topology-based fault-tolerant converters are employed for post-fault operation by considering the minimization of switching losses and reducing the circuit complexity. Moreover, a simplified space vector pulse width modulation (SVPWM) technique is proposed to reduce the computational burden, and a voltage balancing scheme is put forward to increase the DC-bus voltage utilization rate. Simulation studies are carried out in MATLAB/Simulink2017a (MathWorks, Natick, MA, USA) to demonstrate the validity of the proposed hybrid fault-tolerant strategy for DFIG-WT, with the wind speed fluctuation, measurement noises and grid voltage sag taken into consideration.

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“…The adjusting signal driving the adaptation mechanism may be based on rotor flux [13][14][15], back EMF [16,17], or reactive power [18][19][20]. The adaptive flux observers can be classified as deterministic observers, such as the Luenberger observer [21,22], sliding mode observers [23][24][25], and stochastic observers, such as the extended Kalman filter [26][27][28]. Furthermore, neural network based observers are also used for flux estimation [29,30].…”

Section: Introductionmentioning

confidence: 99%

PLL-based Rotor Flux Estimation Method for Sensorless Vector Controlled Squirrel-Cage Induction Generators

Kutija

Sumina

Čolović³

2016

Automatika

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Original scientific paperThis paper presents a new rotor flux estimation method for sensorless vector controlled squirrel-cage induction generators used in wind power applications. The proposed method is based on a phase-locked loop (PLL) and the orthogonality between the rotor flux space vector and its back electromotive force (EMF) space vector. Rotor flux is estimated using stator voltage equations without integrating the back EMF components in the stationary reference frame and the well-known difficulties with the implementation of pure integrators are thus avoided. Moreover, the proposed method ensures successful magnetization of a speed-sensorless squirrel-cage induction generator at nonzero speeds which makes it suitable for wind power applications. Experimental results on a 560 kW squirrel-cage induction generator are presented to confirm the effectiveness and the feasibility of the proposed method.Key words: Induction generator, Flux estimation, Phase locked loop (PLL), Sensorless control, Vector control, Wind power generation Metoda za estimaciju magnetskog toka rotora kaveznih asinkronih generatora bez mjernogčlana brzine vrtnje temeljena na fazno spregnutoj petlji. U radu je predstavljena nova metoda za estimaciju magnetskog toka rotora vektorski upravljanih kaveznih asinkronih vjetrogeneratora. Predložena metoda se temelji na fazno spregnutoj petlji i ortogonalnosti izmeu prostornog vektora magnetskog toka rotora i njemu pripadajućeg prostornog vektora induciranog napona. Magnetski tok rotora se estimira korištenjem naponskih jednadžbi statora u koordinatnom sustavu statora bez integracije komponenti induciranog naponačime su izbjegnuti dobro poznati problemi implementacije integratora. Osim toga, predložena metoda omogućava uspješno magnetiziranje pri vrtnji kaveznog asinkronog generatora bez mjernogčlana brzine vrtnje pa je pogodna za primjenu na vjetrogeneratorima. U radu su prikazani eksperimentalni rezultati za kavezni asinkroni generator snage 560 kW koji potvruju izvedivost i učinkovitost predložene metode.Ključne riječi: asinkroni generator, estimacija magnetskog toka, fazno spregnuta petlja, bezsenzorsko upravljanje, vektorsko upravljanje, proizvodnja električne energije iz vjetra

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Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor

Cited by 118 publications

References 23 publications

A Robust EKF Based Speed Estimator and Fuzzy Optimization Technique for Sensorless Induction Motor Drives

A Robust EKF Based Speed Estimator and Fuzzy Optimization Technique for Sensorless Induction Motor Drives

Sensorless Control of Late-Stage Offshore DFIG-WT with FSTP Converters by Using EKF to Ride through Hybrid Faults

PLL-based Rotor Flux Estimation Method for Sensorless Vector Controlled Squirrel-Cage Induction Generators

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