Numerical Study of Vibration Characteristics for Sensor Membrane in Transformer Oil

Si, Wenrong; Yao, Weiqiang; Guan, Hongyan; Fu, Chenzhao; Yu, Yiting; Su, Shiwei; Yang, Jian

doi:10.3390/en14061662

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…This study focuses specifically on improving the accuracy of oil level measurement with an ultrasonic level sensor that gives measurement range and temperature range, where the design and materials used in the sensor can affect the signal generated and analyzed [19,20]. A major challenge in using ultrasonic signal measurement for oil level measurement is the significant dependence of the ultrasonic signal response on temperature and viscosity [19,[21][22][23]) describing physical phenomena and possible elimination-type methods.…”

Section: Introductionmentioning

confidence: 99%

Identification of the Physical Dependencies of Accurate Oil Level Measurement for Automotive Applications

et al. 2023

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The presented research addresses the problem of dependency analysis of the ultrasonic signal measured by a sensor in an engine oil bath. The dependency analysis is performed on a selected ultrasonic signal sensor solution containing its own generator and an ultrasonic signal receiver detecting the level of the oil in which it is immersed. The influence of the resulting amplitude of the received ultrasonic signal is mainly due to the level of the measured oil level and the oil temperature, as shown by the regression analysis and ANOVA (Analysis of Variance) testing performed. The analyzed dependence of the time determination of the length of the generated ultrasonic signal envelope is given by a set threshold value, which can be dynamically adjusted based on the backtracking evaluation. The analysis results in the form of approximation by the dependency algorithm confirm the assumption of possible standardization of the envelope parameters with the achievement of accuracy up to 99.02%. The analyzed parameters approximated by the temperature and oil level dependence algorithms include the amplitude of the measured signal, steepness of the rising edge, duration of the envelope, and the digitally processed amplitude value.

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Section: Introductionmentioning

confidence: 99%

Identification of the Physical Dependencies of Accurate Oil Level Measurement for Automotive Applications

et al. 2023

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“…In this study, a large number of modes in which core various structures may vibrate were determined experimentally and analytically. In [20], a numerical study of membrane vibration properties in transformer oil was performed. This study showed that mechanical energy is transformed into thermal energy during vibration and that its amplitude continuously decreases with time.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Transformer Vibration Characteristics using the Finite Element Method

Samimi

2022

Scientia Iranica

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Understanding the vibrational characteristics of power transformers is significantly important in their design and monitoring. In this contribution, a model with a multi-physics coupling simulation of the electrical circuit, magnetic field, and solid mechanics is developed to investigate the characteristics of the transformer vibration. After describing the model, the harmonic contents of the vibration signals and their variation in the case of mechanical faults are studied. It is shown that under normal operating conditions, the fundamental vibration frequency of 100 Hz has the maximum amplitude, while in the case of mechanical faults, the amplitudes of 200 Hz and 300 Hz harmonics increase dramatically compared to the fundamental harmonic. The influence of vibration sensor position is investigated too, which indicates that the area near the tank bottom is the best position to gather vibration signals. Moreover, the mechanical resonance frequencies of the transformer, along with their mode shapes, are addressed in this paper. Finally, the influence of mechanical changes on the vibration energy distribution in the tank surface is explored. The results of the paper suggest possible diagnosis methods for condition monitoring of transformers, such as using the vibration energy distribution on the tank surface or analyzing the vibration harmonics.

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Genetic algorithm based artificial neural network and partial least squares regression methods to predict of breakdown voltage for transformer oils samples in power industry using ATR-FTIR spectroscopy

Zandbaaf

Khorrami

Afshar

2022

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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Numerical Study of Vibration Characteristics for Sensor Membrane in Transformer Oil

Cited by 4 publications

References 31 publications

Identification of the Physical Dependencies of Accurate Oil Level Measurement for Automotive Applications

Identification of the Physical Dependencies of Accurate Oil Level Measurement for Automotive Applications

Investigation of Transformer Vibration Characteristics using the Finite Element Method

Genetic algorithm based artificial neural network and partial least squares regression methods to predict of breakdown voltage for transformer oils samples in power industry using ATR-FTIR spectroscopy

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