A current transformer model based on the Jiles-Atherton theory of ferromagnetic hysteresis

Annakkage, U.D.; McLaren, P.G.; Dirks, E.; Jayasinghe, R.P.; Parker, A.D.

doi:10.1109/61.847229

Cited by 193 publications

(61 citation statements)

References 3 publications

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“…The function given in (8) has been used in Ref. [4] in the simulation of current transformers, where a 1 , a 2 and a 3 are constants and a 2 > a 1 . The same function is used to represent the anhysteretic characteristics of the core of the power transformer model, on the basis that the characteristics of the material is likely to be the same, i.e.…”

Section: Jiles-atherton Theorymentioning

confidence: 99%

“…[19]. This process has been adopted in [4] to calculate parameters for the current transformer model. The same methodology was used to derive the parameters for the power transformer model presented here.…”

Section: Determination Of Parametersmentioning

confidence: 99%

“…This has been used in Ref. [4] in the simulation of current transformers, and it has * Corresponding author. Tel.…”

Section: Introductionmentioning

confidence: 99%

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Section: Jiles-atherton Theorymentioning

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Section: Determination Of Parametersmentioning

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Simulation of hysteresis and eddy current effects in a power transformer

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“…However, for some materials, the fitting error is large. (2) Jiles-Atherton model: The advantage of the Jiles-Atherton model [12,13] is its excellent convergence, while the disadvantage is that it is difficult to achieve fast modelling due to numerous undetermined parameters. (3) Maxwell model: Maxwell model [14,15] can improve the convergence rate by reducing the number of subsystems, but the accuracy of the model is reduced accordingly.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator

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As a new actuating material, magnetic controlled shape memory alloys (MSMAs) have excellent characteristics such as a large output strain, fast response, and high energy density. These excellent characteristics are very attractive for precision positioning systems. However, the availability of MSMAs in practical precision positioning is poor, caused by weak repeatability under a certain stimulus. This problem results from the error of a large magnetic hysteresis in an external magnetic field. A suitable hysteresis modelling method can reduce the error and improve the accuracy of the MSMA actuator. After analyzing the original hysteresis modelling methods, three kinds of hysteresis modelling methods are proposed: least squares method, back propagation (BP) artificial neural network, and BP artificial neural network based on genetic algorithms. Comparing the accuracy and convergence rate of three kinds of hysteresis modelling methods, the results show that the convergence rate of least squares method is the fastest, and the convergence accuracy of BP artificial neural networks based on genetic algorithms is the highest.

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“…Various saturated and unsaturated cases are simulated using PSCAD/EMTDC software and validated using SVM classifier implemented in MATLAB. To validate the proposed algorithm, the CT located on transmission line (Figure 2) is analyzed which uses Jiles -Atherton model available in PSCAD/EMTDC software package [25]. Table 1 shows different parameter values which have been chosen in this work to generate CT saturation conditions.…”

Section: System Study and Data Generationmentioning

confidence: 99%

Performance Metrics of Grid Connected Solar PV Power Plant � A Practical Case Study

Kumar¹,

Raju²,

Kar³

et al. 2017

JGE

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During out of zone fault, Current Transformer (CT) saturation leads maloperation in unit type protective schemes. Detection and classification of saturation condition of CT is still a challenging issue. Thus, it is most important to correctly categorize CT saturation condition to increase reliability and stability of protective schemes. The proposed scheme utilizes transmission line CT secondary post fault current signals (sliding window) as an input to SVM. In order to achieve the most optimized classifier, Gaussian Radial Basis Function (RBF) has been used for training of SVM. Feasibility of the proposed scheme has been tested by modelling a part of 220 kV power systems in PSCAD/EMTDC software package. The algorithm is executed in MATLAB software. More than 720 unsaturated and 3600 saturated cases with varying burden resistance, remnant flux, DC component of current, noise penetration to current signal and fault inception angle have been generated and used for validation of the proposed scheme. The proposed scheme effectively discriminates between CT saturated and unsaturated conditions with very high classification accuracy more than 99% for different parameter variations.

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A current transformer model based on the Jiles-Atherton theory of ferromagnetic hysteresis

Cited by 193 publications

References 3 publications

Simulation of hysteresis and eddy current effects in a power transformer

Simulation of hysteresis and eddy current effects in a power transformer

Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator

Performance Metrics of Grid Connected Solar PV Power Plant � A Practical Case Study

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