A new technique for a better sweep frequency response analysis interpretation

Murawwi, Esam Al; Barkat, B.

doi:10.1109/elinsl.2012.6251491

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The most common fault on the transformer is the mechanical deformation due to bad transport or to accidental causes during assembly and coil-coil or shield-coil faults due to forces caused by short-circuit conditions. The measures should therefore be made as well as periodically, even after one of these episodes [10,11].…”

Section: Sfra Testmentioning

confidence: 99%

Improving the Power System safety in Hospitals by means of periodical SFRA tests on Medical Isolation Transformers

Bucci¹,

Ciancetta²,

Fioravanti³

et al. 2020

REPQJ

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The paper has the main aim of demonstrating a possible use of the well consolidated monitoring SFRA (Sweep Frequency Response Analysis) technique for preventive maintenance procedure applied to isolation medical transformers operating in hospitals' group 2 medical locations. In particular, a novel testing platform has been implemented that can perform the monitoring test on IT-M during normal periodical verification provided by specific standard. In the paper, some results of the practical application of such a platform to several transformers in currently in operation in various Italian hospitals have been reported.

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Section: Sfra Testmentioning

confidence: 99%

Improving the Power System safety in Hospitals by means of periodical SFRA tests on Medical Isolation Transformers

Bucci¹,

Ciancetta²,

Fioravanti³

et al. 2020

REPQJ

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“…The phase against frequency graph does not contain as much useful information as the amplitude against frequency graph does, due to the influence of noise in the output signal results and the result of the SFRA measurement. [5] A new technique for a better sweep frequency response analysis interpretation † Evaluate the cross-correlation coefficient of the computed time-frequency distribution. General curve probabilistic, not deterministic.…”

Section: Ref Title the Main Differencementioning

confidence: 99%

“…At LV side, the short simulation is done by changing (R SL from 0.2 to 0 Ω), (L SL from 110 mH to 0.0 H), (C gH from 6 pF to 6 μF), and C SH from 0.2 to 2 μF) at disk no. (5). Based on the calculations of the diagnostic methodology using the CCF technique indicators according to three frequency sub-bands are given in Table 13.…”

Section: Sc-to-ground Fault At LV Sidementioning

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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