Dual Reversible Transformer Model for the Calculation of Low-Frequency Transients

2013 ◽  
Vol 28 (4) ◽  
pp. 2509-2517 ◽  
Author(s):  
Saeed Jazebi ◽  
Francisco de Leon ◽  
Ashkan Farazmand ◽  
Digvijay Deswal
Keyword(s):  
Low Frequency ◽  
Transformer Model

scholarly journals Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4332
Author(s):  
Daniel Jancarczyk ◽  
Marcin Bernaś ◽  
Tomasz Boczar
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Input Data ◽  
Learning Algorithms ◽  
Low Frequency ◽  
Machine Learning Algorithms ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Distribution Transformer ◽  
Transformer Model

The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task.

Improved low-frequency transformer model based on Jiles–Atherton hysteresis theory

2017 ◽  
Vol 11 (4) ◽  
pp. 915-923 ◽  
Author(s):  
Mi Zou ◽  
Wenxia Sima ◽  
Ming Yang ◽  
Licheng Li ◽  
Qing Yang ◽  
...  
Keyword(s):  
Low Frequency ◽  
Model Based ◽  
Transformer Model

An Improved Low-Frequency Transformer Model for Use in GIC Studies

2004 ◽  
Vol 19 (2) ◽  
pp. 643-651 ◽  
Author(s):  
W. Chandrasena ◽  
P.G. McLaren ◽  
U.D. Annakkage ◽  
R.P. Jayasinghe
Keyword(s):  
Low Frequency ◽  
Transformer Model

Study on Methodology Detecting Turn-to-Turn Short Circuit of Transformer

2013 ◽  
Vol 805-806 ◽  
pp. 876-879
Author(s):  
Yu Sheng Quan ◽  
Xin Zhao ◽  
Guang Chen ◽  
En Ze Zhou
Keyword(s):  
Short Circuit ◽  
Information Source ◽  
Low Frequency ◽  
Extreme Value ◽  
Frequency Characteristics ◽  
Criterion Function ◽  
Novel Method ◽  
Transient Voltage ◽  
Transformer Model ◽  
Short Circuit Fault

A novel method for detecting turn-to-turn short circuit is proposed in this paper. This detection makes the transient voltage and current applied in the transformer port as the information source. The transformer model is structured according to its low-frequency characteristics. By comparison the extreme value of the criterion function proposed in this paper, the transformer turn-to-turn short circuit fault can be detected. Based on good results in the simulation, the methodology is proved effective and practical.

Once again about the Steinmetz transformer model and the ongoing subdivision of its leakage inductance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sergey E. Zirka ◽  
Yuriy I. Moroz ◽  
Cesare Mario Arturi
Keyword(s):  
Phase Synchronization ◽  
Low Frequency ◽  
Electromagnetic Transients ◽  
Inrush Current ◽  
Content Type ◽  
Leakage Inductance ◽  
Core Saturation ◽  
Transformer Model ◽  
Correct Understanding ◽  
Practical Implications

Purpose Despite its well-founded criticism and lack of proper justification under core saturation conditions, the T-equivalent transformer model (Steinmetz scheme) is obviously championing in the literature. This educational paper aims to explain in a simple manner the limitations of the T-model of a low-frequency transformer and critically analyses some attempts to improve it. Design/methodology/approach Using a simplified examination of magnetic fluxes in the core and windings and using the modeling in ATPDraw, it is shown that transient transformer models with the indivisible leakage inductance allow circumventing the drawbacks of the T-model. Findings The authors show the absence of valid grounds for subdividing the leakage inductance of a transformer between its primary and secondary windings. The connection between the use of individual leakage inductances and inaccurate prediction of inrush current peaks is outlined as an important example. Practical implications The presented models can be used either as independent tools or serve as a reference for subsequent developments. Social implications Over generations, the habitual transformer T-equivalent is widely used by engineers and Electromagnetic Transients Program experts with no attention to its inadequacy under core saturation conditions. Having studied typical winding configurations, the authors have shown that neither of them has any relation to the T-equivalent. Originality/value This educational paper will contribute to the correct understanding of the transients occurring in a transformer under abnormal conditions such as inrush current or ferroresonance events, as well as during an out-of-phase synchronization of step-up generator transformers.

Retrofitting the BCTRAN Transformer Model With Nonlinear Magnetizing Branches for the Accurate Study of Low-Frequency Deep Saturating Transients

2018 ◽  
Vol 33 (5) ◽  
pp. 2344-2353 ◽  
Author(s):  
Ming Yang ◽  
Reza Kazemi ◽  
Saeed Jazebi ◽  
Digvijay Deswal ◽  
Francisco de Leon
Keyword(s):  
Low Frequency ◽  
Transformer Model

A Study on the Fine Structure of the Lateral Line Organ of the Sea Eel

1973 ◽  
Vol 31 ◽  
pp. 648-649
Author(s):  
K. Hama
Keyword(s):  
Fine Structure ◽  
Electron Microscope ◽  
Lateral Line ◽  
Low Frequency ◽  
Sensory Cells ◽  
Apical Surface ◽  
Voltage Electron ◽  
Sensory Epithelia ◽  
Low Frequency Vibration ◽  
Transmission Electron

The lateral line organs of the sea eel consist of canal and pit organs which are different in function. The former is a low frequency vibration detector whereas the latter functions as an ion receptor as well as a mechano receptor.The fine structure of the sensory epithelia of both organs were studied by means of ordinary transmission electron microscope, high voltage electron microscope and of surface scanning electron microscope.The sensory cells of the canal organ are polarized in front-caudal direction and those of the pit organ are polarized in dorso-ventral direction. The sensory epithelia of both organs have thinner surface coats compared to the surrounding ordinary epithelial cells, which have very thick fuzzy coatings on the apical surface.

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