Unscented Kalman filter for non-linear estimation of induction machine parameters

Lalami, Abdelhalim; Wamkeue, R.; Kamwa, Innocent; Saad, Mohamad; Beaudoin, J. J.

doi:10.1049/iet-epa.2012.0026

Cited by 26 publications

(10 citation statements)

References 15 publications

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“…Several methods of parameter identification of the induction machine have been proposed in the literature [8][9][10][11]. These can be divided into two main categories; signal-based [12,13] and model-based techniques where machine parameters are identified based on external measurements of voltage, current, speed, and/or torque [14][15][16][17][18]. In this case, different sets of parameter values may be obtained depending on whether the machine model is identifiable or not [4,15,19].…”

Section: Introductionmentioning

confidence: 99%

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

Alturas

Gadoue

Zahawi

et al. 2016

IEEE Trans. Energy Convers.

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Newcastle University ePrints -eprint.ncl.ac.uk Alturas AM, Gadoue SM, Zahawi B, Elgendy MA. On the Identifiability of Steady State Induction Machine Models using External Measurements. IEEE Transactions on Energy Conversion 2015. Abstract-A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the Alternating Conditional Expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1 kW three-phase induction machine in conjunction with the Levenberg-Marquardt (L-M) algorithm which is developed and applied here for this purpose. Index Terms: induction motor (IM), identifiability analysis, parameter identification.A. M. Alturas is with the School

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Section: Introductionmentioning

confidence: 99%

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

Alturas

Gadoue

Zahawi

et al. 2016

IEEE Trans. Energy Convers.

View full text Add to dashboard Cite

show abstract

“…(b) The extended Calman filter [6] is recursive estimation method [7] . The estimated value of current state is calculated through the estimated value at the state of a moment before and the observed value of current, and this method is used in linear stochastic systems [8][9] . (c)MRAS is used to identified the motor parameter, and a suitable adaptive law is found [10][11] .…”

Section: Introductionmentioning

confidence: 99%

A Review on Parameters Identification Methods for Asynchronous Motor

Xing¹,

Zeng²,

Liu³

et al. 2015

ijacsa

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Abstract-The decoupling of excitation current and torque current is realized by Vector control so that the speed regulating performance of asynchronous motor is comparable with that of dc motor. The control precision is directly affected by accuracy of parameter identification in asynchronous motor. In this paper, based on the existing literatures, the existed parameters identification methods both online and offline are analyzed and compared, and the advantages and disadvantages of the various algorithms are listed it tables. Therefore, a comprehensive identification method of adjustable model which makes the least square method as adaptive method of model reference is presented. Finally, the outlook of developing direction for parameters identification of asynchronous motor are put forward to.

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“…There have been several studies of power quality events identification using wavelets and fuzzy logic [15]- [17] as well as of load identification by measuring the harmonic impedances and the current and voltage harmonic parameters [18], [19]. Furthermore, there has been work on the system identification of specific electric machines using active filters and data summarization [20]- [23]. Recent technological advances have augmented the performance of power quality monitors and some smart meters beyond just monitoring power quality events [24]- [26].…”

Section: Introductionmentioning

confidence: 99%

Defining the Unique Signatures of Loads Using the Currents’ Physical Components Theory and $Z$-Transform

Calamero

Beck

Shmilovitz

2015

IEEE Trans. Ind. Inf.

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A method to extract unique features from measured waveforms of nonlinear loads is presented. These features can be used for the identification of loads connected to the grid. The method is based on the currents' physical components (CPC) electric power transport theory combined with the Z-transform (for implementation using digital signal processing (DSP). A set of admittance-based Z-transform functions that reflect the current physical components suggested by the CPC is constructed. The resulting transfer functions are shown to reflect the electric physical significance, and are used for electric load and machine identification through its electric characteristics. In this paper, the Z-transform analytic expressions are developed and extended along with their physical comprehension. Moreover, the strength of the theory over more traditional spectral analysis is explained. The method presented in this paper is demonstrated and analyzed using real-world measured waveforms (measured by power quality monitors). Index Terms-Load identification, power transfer theory, smart grid.1551-3203 worked with R&D for the Israel Air-Force (IAF), where he developed power processing topologies for avionic systems. During 1997-1999, he was a Post-Doctorate Fellow with New York Polytechnic University, Brooklyn, NY, USA. Since 2000, he has been with the Faculty of Engineering, Tel-Aviv University, where he heads the Power Electronics and Power Quality Research Group. He has authored and coauthored over 100 papers, of which over 40 are in refereed journals. His research interests include general circuit theory, switched-mode converters, power quality, special applications of power electronics (such as for alternative energy sources) powering of autonomous sensor networks, and implanted medical devices.

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Unscented Kalman filter for non-linear estimation of induction machine parameters

Cited by 26 publications

References 15 publications

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

A Review on Parameters Identification Methods for Asynchronous Motor

Defining the Unique Signatures of Loads Using the Currents’ Physical Components Theory and $Z$-Transform

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