Robust transfer function identification via an enhanced magnitude vector fitting algorithm

Tommasi, Luciano De; Gustavsen, Bjørn; Dhaene, Tom

doi:10.1049/iet-cta.2009.0025

Cited by 15 publications

(22 citation statements)

References 21 publications

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“…In addition to that an indirect measurement method based on the FRA response was explained in [24] for the determination of the C s value. The effective capacitance can, therefore, be expressed as a function of the series capacitance, distribution constant α and the number of sections by (10), so that we can identify the series capacitance and distribution constant using this equation…”

Section: Implementation Of Ba To Estimate the Model Parametersmentioning

confidence: 99%

BA to construction of equivalent circuit of a transformer winding from frequency response analysis measurement

Chaouche¹,

Houassine²,

Moulahoum³

et al. 2018

IET Electric Power Applications

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The calculation of high-frequency model parameters of a winding is crucial for the integrity assessment of transformer winding damages and its severity. Bat algorithm (BA) is one of the modern and efficient methods used in computational intelligence due to its efficiency to obtain better results for complicated non-linear problems. In this study, an iterative method to ensure the mutually coupled equivalent model synthesis of an actual isolated transformer winding is proposed via bat search algorithm based on frequency response analysis data measurement (resonance frequencies, equivalent inductance and effective capacitance). The equivalent ladder network synthesis of transformer winding using the BA is based on the construction of the search space of the model parameters, thus each model element to be identified must be limited between two bounds (the upper and lower bounds) based on constraints and inequalities defining the model parameters. The effectiveness of the BA was tested by simulation and experimentally on two case studies of two isolated transformer windings with an air core. Furthermore, a comparative study of BA, particle swarm optimisation and the magnitude vector fitting was made in terms of accuracy and convergence speed.

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Section: Implementation Of Ba To Estimate the Model Parametersmentioning

confidence: 99%

BA to construction of equivalent circuit of a transformer winding from frequency response analysis measurement

Chaouche¹,

Houassine²,

Moulahoum³

et al. 2018

IET Electric Power Applications

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“…This technique can be used to identify diagnosis fault of power transformer and overhead transmission line. [11] Investigation of the transformer winding high frequency parameters identification using particle swarm optimisation (PSO) method…”

Section: Ref Title the Main Differencementioning

confidence: 99%

Proposed diagnostic methodology using the cross‐correlation coefficient factor technique for power transformer fault identification

Saleh

El-Hoshy

Gouda

2017

IET Electric Power Applications

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This study investigates the impact of electrical parameter variation of a high-frequency transformer model on its sweep frequency response analysis (SFRA) signature to help in classification and interpretation. The simulations have been done using MATLAB and compared with the reference data. The results of SFRA measurements are repeatable up to and beyond 1MHz. The proposed diagnostic methodology using the Cross-Correlation Coefficient Factor (CCF) is used to identify the transformer faults. CCF used to measure the degree of relationship between two variables that establish a relation between the predicted and actual data set. The results of this proposed methodology using the CCF compared with existing Chinese Standard factor (CSF) indicate that, the proposed method is valid to identify the transformer faults. Characteristics of the proposed scheme are fully analyzed by extensive MATLAB simulation studies that clearly reveal that this method can accurately identify the transformer faults compared with CSF. And also does not affected by different fault conditions such as transformer normal condition, Turn to Turn Fault for both HV, LV sides, Axial Fault and/or Radial Faults on both sides, Short Circuit Fault between H.V and L.V Sides, Short Circuit to Ground Fault for both HV, LV sides.

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“…w are the symmetrical basis functions allowing for an accurate magnitude fitting via spectral factorisation. Further details on the selection of basis functions are referred to [9].…”

Section: ( )mentioning

confidence: 99%

“…parameters of coupled lines are usually given in the form of tables. In the implementation of MRA, the magVF [2,9] algorithm provides a robust procedure for the identification of these data samples. First, a relaxed vector fitting algorithm [4] is employed to set up the rational form with respect to frequency, as given by…”

mentioning

confidence: 99%

Thermal sensitivity analysis of on‐chip interconnects

Tang

et al. 2014

Electron. lett.

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A new method for time-domain sensitivity analysis of on-chip coupled interconnects with non-uniform substrate temperature profiles is presented. The parametric macromodelling is performed via multivariate rational approximation using magnitude vector fitting algorithms. The waveform relaxation algorithm with transverse partitioning is employed for efficient sensitivity analysis of coupled interconnects. With these techniques, the thermal sensitivity of on-chip global interconnects arising from typical substrate thermal profiles are investigated.

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Robust transfer function identification via an enhanced magnitude vector fitting algorithm

Cited by 15 publications

References 21 publications

BA to construction of equivalent circuit of a transformer winding from frequency response analysis measurement

BA to construction of equivalent circuit of a transformer winding from frequency response analysis measurement

Proposed diagnostic methodology using the cross‐correlation coefficient factor technique for power transformer fault identification

Thermal sensitivity analysis of on‐chip interconnects

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