Parameter identification of the transformer winding based on least-squares method

Jin, Enshu; Liu, L.L.; Zhang, Bo; Klimek, A.

doi:10.1109/pes.2008.4596453

Cited by 20 publications

(8 citation statements)

References 0 publications

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“…This can be seen by the equations (7) to (9). The system matrices are in a nonlinear relation with respect to the vector x, as can be seen by the equations (1) to (4). This is yet no problem, as the system is linear with respect to the input/output relation.…”

Section: Transformer Modelmentioning

confidence: 99%

“…The estimation of transformer parameters has seen different developments over the past years [3], [4], [5], [6], [7], [8], [9], [10]. Figure 1 depicts the structure of a single core transformer.…”

Section: Introductionmentioning

confidence: 99%

“…In on-line methods the measurements are taken during the operation of the transformer where no special experiments can be performed. Several methods in this field recast the estimation problem into the form of an over determined equation system with respect to the parameters of interest [3], [4]. Then the Moore-Penrose inverse (pseudo inverse) is used to solve the equation system in a least squares sense.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian estimation of electrical transformer parameters

Neumayer

Bretterklieber

Zangl³

2014

2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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Electrical transformers are among the oldest and most well studied devices in electrical engineering. Accurate knowledge of the transformers parameters is still required for modeling and simulation of the behavior of the transformer in all scenarios -for the single component as well as for the transformer within a larger system. Measuring the transformer parameters requires several experiments covering both, AC and DC-current measurements. In this work we will present a model based approach to estimate transformer parameters from a reduced set of measurements. This includes the estimation of the DC resistance of the transformer from AC measurements only. The methodology is based on the Bayesian framework, which provides a powerful tool for gaining knowledge about the parameters of interest from indirect measurement data.

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Section: Transformer Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian estimation of electrical transformer parameters

Neumayer

Bretterklieber

Zangl³

2014

2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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“…The RLS can be applied in different frequencies providing (6) and 7. In (6) and 7:  represents the fundamental frequency of supply voltage, t  corresponds to the sample time. Thus, the RLS estimates the leakage and magnetization impedances for each frequency.…”

Section: Recursive Least Squaresmentioning

confidence: 99%

“…The model was found to be more accurate than existing model, which was based on magnetic theory, though at the expense of complexity of programming. A parameter identification method using the least squares based on orthogonal decomposition to identify the leakage inductances and resistances of transformer winding was made by [6]. The algorithm provided a good foundation for online identification of transformer parameters and offers great potential for practical application.…”

Section: Introductionmentioning

confidence: 99%

The use of recursive least square to determine the model parameters of a transformer in different frequencies

Araújo¹,

Florentino²,

Ferreira³

et al. 2013

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)

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This paper presents an alternative approach for determining the parameters of single-phase two-winding transformer. The dynamic parameters of a transformer, including its inductances and resistances are estimated by the application of recursive least squares algorithm on the measured data taken from the terminal voltage and current. The results show that the resistances increase and the inductances decrease with the elevation of the frequency. The model properly represents the characteristics of the original transformer. The accuracy of the results is also verified by simulation tests.

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