An improved second‐order kinetic model for degradation analysis of transformer paper insulation under non‐uniform thermal field

Liu, Jiefeng; Kuo, Jimmy; Wang, Qingyin; Zhang, Heng; Zhang, Enze; Fan, Xianhao; Zhang, Yiyi

doi:10.1049/hve2.12226

Cited by 5 publications

(7 citation statements)

References 28 publications

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“…Where 𝐷𝑃 0 and 𝐷𝑃 𝑡 are the Nomex paper DP at initial time and time t respectively, t is the ageing time, 𝑘 1 and 𝑘 2 are the rate of reaction constants. Furthermore, an improved secondorder kinetic model is proposed in [38] to investigate the insulating paper ageing state under an axial temperature gradient. In [30], the authors estimate the effect of moisture, oxygen, and hot-spot temperature on the lifespan of the transformer with TUP and Kraft paper.…”

Section: A Mathematical Model Conceptmentioning

confidence: 99%

“…This expression is known as ABB's equation. 𝐻 𝑝 = 2.06915𝑒 −0.0297𝜃 𝐻𝑆 × (𝐻 0 ) 0.4089𝜃 𝐻𝑆 0.09733 (38) Where 𝐻 0 in ppm is the humidity of the insulating oil, and 𝐻 𝑝 in % is the insulating paper moisture content. In [174], a novel and easily accessible technique was used to estimate the oil-immersed insulating paper moisture content.…”

Section: B Moisture and Ageingmentioning

confidence: 99%

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Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

Adekunle,

Fofana,

Picher

et al. 2024

IEEE Access

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In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment's safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.INDEX TERMS Insulating paper, ageing, degree of polymerization, prognostics and health management, machine learning model.

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Section: A Mathematical Model Conceptmentioning

confidence: 99%

Section: B Moisture and Ageingmentioning

confidence: 99%

Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

Adekunle,

Fofana,

Picher

et al. 2024

IEEE Access

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“…There is no unified conclusion on the specific mechanism and chemical reaction process. It is generally agreed that methanol is formed due to 1, 4‐β cleavage of glycosidic bonds in cellulose [61], 1, 4‐β Glycosidic bond is a chemical bond connecting glucose monomers on the cellulose chain.…”

Section: Formation Mechanism Of Methanolmentioning

confidence: 99%

A review on a novel method for aging evaluation of transformer insulating paper based on methanol

Chen

Sun

Cheng

et al. 2023

IET Generation Trans & Dist

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The insulation paper is a crucial factor for evaluating the insulation status of transformers. The traditional evaluation methods for insulation paper are dissolved gas component content analysis (CO and CO 2 ) and furfural (2-FAL) content analysis in oil. The detection principle of the former leads to its low accuracy, while the field application effect of the latter is not ideal due to the easy oxidation of furfural and low content. Methanol, result of its good stability and high production compared with other marker products (CO, CO 2 , 2-FAL) has been investigated as a novel marker for aging evaluation of transformer insulating paper in recent years. The results of the investigation indicated that there is a good correlation between the content of methanol in oil and the degree of polymerization under laboratory conditions, which reflected the insulation aging state of the transformer insulating paper more accurately. This paper summarizes the current research status of methanol formation mechanism, detection methods and aging evaluation in insulating oil, and points out the key problems to be solved and development prospects, hoping to provide relevant reference for aging evaluation of oil immersed power transformer.

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“…In the transformer, the oil‐paper insulation undertakes the functions of supporting windings, isolating components, and heat dissipation [2]. According to the existing research [3], the ageing and dampness of oil‐paper insulation will reduce its property and performance. While the ageing and dampness of the oil‐paper insulation exceed the security threshold, the oil‐paper insulation system will be broken down, resulting in transformer damage and grid debacle [4].…”

Section: Introductionmentioning

confidence: 99%

Microscopic mechanisms analysis of various dielectric response processes in oil‐paper insulation with different insulating states

Jiang,

Liu,

Fan

et al. 2024

High Voltage

Self Cite

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Various dielectric response processes in the oil‐paper insulation are sensitively affected by the insulating states (ageing degrees and moisture contents). However, the existing research is still incomplete in revealing the microscopic mechanisms of various dielectric response processes in oil‐paper insulation with different insulating states. Given this issue, the genetic algorithm is first adopted to extract the Dissado–Hill (D–H) model parameters by simulating the frequency domain spectroscopy (FDS) of oil‐paper insulation. Then, the change laws of the extracted D–H model parameters are adopted to reveal the microscopic mechanisms of various dielectric response processes. Microscopic mechanisms of four dielectric response processes are studied, which are quasi‐dc relaxation, loss peak relaxation, optical frequency relaxation, and DC conductance. Meanwhile, due to the dielectric response processes being dominated by various polar particles (methanol, ethanol, furfural, and water molecule), the contents of various ageing by‐products dissolved in the insulating oil are measured to support the above analysis. In this respect, a dielectric theoretical reference for the FDS technique to research the insulating states of oil‐paper insulation is provided.

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An improved second‐order kinetic model for degradation analysis of transformer paper insulation under non‐uniform thermal field

Cited by 5 publications

References 28 publications

Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

A review on a novel method for aging evaluation of transformer insulating paper based on methanol

Microscopic mechanisms analysis of various dielectric response processes in oil‐paper insulation with different insulating states

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