Investigation on gas generation characteristics in transformer oil under vibration

Guo, Chong; Zhang, Qiaogen; Zhang, Rui; He, Xiaohui; Wu, Zhicheng; Wen, Tao

doi:10.1049/gtd2.12654

Cited by 8 publications

(8 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, H 2 is produced when OIE experiences faults such as corona discharge, water electrolysis, rusting, severe overloading, high-temperature overheating, or spark discharge . C 2 H 2 is commonly used as an indicator gas for identifying high-energy discharge faults in OIE . For the offline or online DGA method, the process of oil–gas separation typically precedes gas detection and analysis due to the solubility of these fault gases in insulation oil, and the separation quality directly influences the accuracy and efficiency of subsequent detection and fault diagnosis processes.…”

Section: Industrial Gas Sensing Target and Requirementmentioning

confidence: 99%

Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications

Wu,

Li,

Yang

et al. 2024

ACS Sens.

View full text Add to dashboard Cite

The escalating development and improvement of gas sensing ability in industrial equipment, or “machine olfactory”, propels the evolution of gas sensors toward enhanced sensitivity, selectivity, stability, power efficiency, cost-effectiveness, and longevity. Two-dimensional (2D) materials, distinguished by their atomic-thin profile, expansive specific surface area, remarkable mechanical strength, and surface tunability, hold significant potential for addressing the intricate challenges in gas sensing. However, a comprehensive review of 2D materials-based gas sensors for specific industrial applications is absent. This review delves into the recent advances in this field and highlights the potential applications in industrial machine olfaction. The main content encompasses industrial scenario characteristics, fundamental classification, enhancement methods, underlying mechanisms, and diverse gas sensing applications. Additionally, the challenges associated with transitioning 2D material gas sensors from laboratory development to industrialization and commercialization are addressed, and future-looking viewpoints on the evolution of next-generation intelligent gas sensory systems in the industrial sector are prospected.

show abstract

Section: Industrial Gas Sensing Target and Requirementmentioning

confidence: 99%

Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications

Wu,

Li,

Yang

et al. 2024

ACS Sens.

View full text Add to dashboard Cite

show abstract

“…As a result, it has become increasingly prevalent in the field. Currently, the traditional diagnostic methods for dissolved gases in transformer oil include the three-ratio method [ 5 ] and the Duval Triangle method [ 6 ]. However, these approaches suffer from shortcomings such as insufficient coding and excessive absoluteness, leading to a higher rate of misjudgment.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method for a Power Transformer Based on Multi-Scale Approximate Entropy and Optimized Convolutional Networks

Shang,

Liu,

Wei

et al. 2024

Entropy

View full text Add to dashboard Cite

Dissolved gas analysis (DGA) in transformer oil, which analyzes its gas content, is valuable for promptly detecting potential faults in oil-immersed transformers. Given the limitations of traditional transformer fault diagnostic methods, such as insufficient gas characteristic components and a high misjudgment rate for transformer faults, this study proposes a transformer fault diagnosis model based on multi-scale approximate entropy and optimized convolutional neural networks (CNNs). This study introduces an improved sparrow search algorithm (ISSA) for optimizing CNN parameters, establishing the ISSA-CNN transformer fault diagnosis model. The dissolved gas components in the transformer oil are analyzed, and the multi-scale approximate entropy of the gas content under different fault modes is calculated. The computed entropy values are then used as feature parameters for the ISSA-CNN model to derive diagnostic results. Experimental data analysis demonstrates that multi-scale approximate entropy effectively characterizes the dissolved gas components in the transformer oil, significantly improving the diagnostic efficiency. Comparative analysis with BPNN, ELM, and CNNs validates the effectiveness and superiority of the proposed ISSA-CNN diagnostic model across various evaluation metrics.

show abstract

“…The bubble nuclei are expanded, resulting in the initiation of macroscopic bubbles. That is, the cavitation process caused by vibration appears in transformer oil [13].…”

Section: Introductionmentioning

confidence: 99%

“…FIGURE13 Relationship among initial pressure threshold and gas content, water content, and temperature of transformer oil. (a) W=14.9 ppm, T=25 ℃.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Characteristics and Mechanism of Bubble Initiation in Transformer Oil Induced by Vibration

Guo

Zhang

Jing-en

et al. 2023

Preprint

View full text Add to dashboard Cite

Operating experience has shown that vibration can cause pressure fluctuations and initiate bubbles at the oil gap, which seriously threatens the insulation properties of oil-immersed power equipment. The main aim of this study was to determine the characteristics and mechanism of bubble initiation at the transformer oil gap under vibration in this paper. The results show the existence of the vibration-dependent bubbling phenomenon at the oil gap. Interestingly, bubbles initiate during the process of gap expansion, and the evolution of bubble morphology was further analyzed. The threshold characteristic of the bubble initiation process was found, and the relevant influencing factors were experimentally obtained. In the range considered in this study, higher gas content, higher temperature, and lower moisture content are conducive to bubble initiation at the oil gap under vibration, while vibrational frequency does not affect the threshold characteristic of bubble initiation process. The vibration-dependent bubbling phenomenon can be attributed to the vibration-induced cavitation process caused by the decrease in pressure at the oil gap. The influence mechanism of the above factors on the threshold characteristics of bubble initiation was further discussed. This study contributes to providing a reference for risk assessment of abnormal vibration of oil-immersed power equipment.

show abstract

Investigation on gas generation characteristics in transformer oil under vibration

Cited by 8 publications

References 43 publications

Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications

Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications

A Novel Fault Diagnosis Method for a Power Transformer Based on Multi-Scale Approximate Entropy and Optimized Convolutional Networks

Characteristics and Mechanism of Bubble Initiation in Transformer Oil Induced by Vibration

Contact Info

Product

Resources

About