Transformer Model for Inrush Current Calculations: Simulations, Measurements and Sensitivity Analysis

Chiesa, Nicola; Mork, B.A.; Høidalen, Hans Kristian

doi:10.1109/tpwrd.2010.2045518

Cited by 97 publications

(63 citation statements)

References 15 publications

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“…As can be observed, the simulated curve using the modified parameters can match the measured one precisely. Just like [19], the first crest difference, which describes the difference between the first crests of the two curves, is adopted to measure the simulation accuracy. The first crest difference is reduced to 1.1% from the original 45%, which indicates that the modified J-A parameters can generate much more accurate inrush current than the original J-A parameters.…”

Section: Resultsmentioning

confidence: 99%

“…In [19], the physical topology of the transformer is used to formulate the transformer model. Each of the three main legs and the two outer yokes are modeled with a J-A hysteretic model.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…In [1], the author proposed a method for simulating magnetizing inrush currents in power transformers by taking the transformer operating conditions, including transformer loading power factors, switching-on angles, and remanent flux into consideration. References [19,20] present a transformer model for low-and mid-frequency transient studies with a focus on the behavior An automatic J-A parameter tuning method is presented in Section 4. The experimental results are shown and analyzed in Section 5.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic J–A Model Parameter Tuning Algorithm for High Accuracy Inrush Current Simulation

Wen

Jing-zhuo

2017

Energies

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Inrush current simulation plays an important role in many tasks of the power system, such as power transformer protection. However, the accuracy of the inrush current simulation can hardly be ensured. In this paper, a Jiles-Atherton (J-A) theory based model is proposed to simulate the inrush current of power transformers. The characteristics of the inrush current curve are analyzed and results show that the entire inrush current curve can be well featured by the crest value of the first two cycles. With comprehensive consideration of both of the features of the inrush current curve and the J-A parameters, an automatic J-A parameter estimation algorithm is proposed. The proposed algorithm can obtain more reasonable J-A parameters, which improve the accuracy of simulation. Experimental results have verified the efficiency of the proposed algorithm.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In [19], the physical topology of the transformer is used to formulate the transformer model. Each of the three main legs and the two outer yokes are modeled with a J-A hysteretic model.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic J–A Model Parameter Tuning Algorithm for High Accuracy Inrush Current Simulation

Wen

Jing-zhuo

2017

Energies

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“…The model to be used is the hybrid transformer (Hybrid transformer), it is a model that was designed to cover the shortcomings of other existing models, it is a model capable of providing a response both in permanent regime and in transitory regime, This way it is possible to represent with a good precision the effects of the electric circuit and the magnetic circuit [6].…”

Section: Transformersmentioning

confidence: 99%

A Novel Approach for the Detection, Classification and Localization of Transmission Lines Faults using Wavelet Transform and Support Vector Machines Classifier

S.Wani¹,

Singh²

2018

IJET

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Over the time, techniques have been developed whose approach has been linked mainly to the diagnosis of faults, which is why over the years these techniques have been improved little by little in order to complement or at best cases innovate the traditional methods used for the detection of faults from the mathematical point of view relying mainly on sophisticated methods and some of them related to artificial intelligence. Taking into account the aforementioned, in this paper we propose the use one of the branches of artificial intelligence, specifically automatic learning through the tool known as Support Vector Machines (SVM) to find a method with which is feasible to identify and classify the type of fault. For the creation of the mathematical model it is essential to have a database. The database consists of input data and output data, the input data are the detail coefficients obtained from the decomposition of the current and voltage signals using the Discrete Wavelet Transform (DWT). Meanwhile, the output data are the labels assigned and with which the model can identify and classify the different types of faults. Both current signals and voltage signals are generated based on an extensive simulation of faults along the longest transmission line that has a test system.

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“…00 simulation in power transformers. The equivalent electric circuit can be constructed using duality transformation of the predefined magnetic circuit [20]. The magnetic circuit of the transformer is constructed based on the flux paths in the windings and the core.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Approach for Simulation of Inrush Current in Three-phase Transformers Considering Hysteresis Effects

Naghizadeh

Vahidi

Hosseinian

2016

Electric Power Components and Systems

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Transformer Model for Inrush Current Calculations: Simulations, Measurements and Sensitivity Analysis

Cited by 97 publications

References 15 publications

Automatic J–A Model Parameter Tuning Algorithm for High Accuracy Inrush Current Simulation

Automatic J–A Model Parameter Tuning Algorithm for High Accuracy Inrush Current Simulation

A Novel Approach for the Detection, Classification and Localization of Transmission Lines Faults using Wavelet Transform and Support Vector Machines Classifier

An Adaptive Approach for Simulation of Inrush Current in Three-phase Transformers Considering Hysteresis Effects

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