State-of-the-art review on asset management methodologies for oil-immersed power transformers

Jin, Lan; Kim, Dowon; Abu-Siada, A.

doi:10.1016/j.epsr.2023.109194

Cited by 18 publications

(3 citation statements)

References 131 publications

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“…Time series prediction based on the LSTM method for dissolved gas level in insulation was presented in [84]. In [202], the authors proposed a new asset management technique for power transformer insulating liquid by utilizing online DGA data. Their proposed model consists of two different submodules, which are fault diagnostics and life management modules, these two modules were made to undergo a training process using the CNN machine learning framework.…”

Section: Insulating Liquid Condition 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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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: Insulating Liquid Condition 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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“…With the development of science and technology, the application of high-efficiency transformers will promote more efficient and low-carbon operations for transmission and transformation projects [26]. Its full life cycle cost is calculated as follows [27,28]:…”

Section: High-efficiency Transformersmentioning

confidence: 99%

Comprehensive Evaluation and Scheme Optimization for Power Transmission and Transformation Projects with the Adoption of Carbon Reduction Technologies

Zhao,

Chen,

Jia

et al. 2024

Energies

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To investigate the economic impact of carbon reduction technology on power transmission and transformation projects, carbon reduction technologies adapted to power transmission and transformation projects were investigated, and the evaluation indicator system for cost-benefit in power transmission and transformation projects was established based on AHP (Analytic Hierarchy Process) and EWM (Entropy Weight Method). Taking 110 kV and 220 kV transmission and transformation projects in a province located in northwest China as an example, a sensitivity analysis was carried out for the weight of each carbon reduction technology. Additionally, based on the evaluation indicator system, eight alternative carbon reduction schemes were proposed, and the net present value and scheme scores were analyzed with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). The results showed that in the criterion layer of the 110 kV case, the highest proportion of weight is “high-efficiency transformer”, accounting for 34.12%, and in the indicator layer, the highest proportion of weight is “ parasitic losses”, accounting for 12.35%. After conducting error analysis on the 110 kV and 220 kV cases, it was found that the errors were within an acceptable range both in the criterion layer and index layer. Moreover, it is expected to achieve greater economic benefits through lower costs according to Scheme VI, and due to the limitations of carbon reduction technologies and the lack of a relevant policy system, it is difficult to achieve the goal of covering costs with benefits for the eight schemes studied.

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“…The proper functioning stability of the transformer is extremely critical. , However, after long hours of work or internal overheating failure, aging failure, and partial discharge failure, the cracking of liquid-insulating oil in an oil-immersed power transformer produces characteristic gases of different types and concentrations (H 2 , CO, C 2 H 2 , C 2 H 4 , etc. ). , Therefore, the detection of characteristic gas composition and concentration in oil by the dissolved gas analysis (DGA) technology is helpful to prevent and detect internal faults of transformers and ensure the operation status of transformers. − Gas sensor detection is one of the most widely used techniques for detecting dissolved gases in oil. This method infers the gas composition and content by the response of the sensor to the sample to be tested; the utility model has the advantages of a portable device, simple operation, rapid detection, etc. , The gas sensors work on the principle of a gas-sensitive reaction between the material and the gas.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Adsorption Performance of Gases in Oil on a Cr₃-Modified SnS₂ Monolayer Based on the First Principles

Zhang,

Feng,

Zhang

et al. 2023

Langmuir

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Dissolved gas analysis (DGA) is the most commonly used transformer fault diagnosis technology at present. In this paper, according to the method of first principles of density function theory (DFT), the gas-sensitive mechanisms of four oilsoluble characteristic gases (H 2 , CO, C 2 H 2 , C 2 H 4 ) on intrinsic SnS 2 and Cr 3 −SnS 2 were studied. The adsorption energy and electron transfer were calculated, and the density of states, energy bands, and recovery times were analyzed. It was concluded that H 2 and C 2 H 4 were physisorbed on the Cr 3 −SnS 2 monolayer, while CO and C 2 H 2 were chemisorbed. It is believed that the Cr 3 −SnS 2 material can be used in gas sensing for CO and C 2 H 2 . Cr 3 −SnS 2 is expected to serve as a gas detector for the detection of CO with both a good response and reusability. Therefore, Cr 3 −SnS 2 has very promising applications in the evaluation of the operation of oil-immersed transformers. This study will provide some help and inspiration for the development of the Cr 3 −SnS 2 monolayer in gas-sensitive materials.

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State-of-the-art review on asset management methodologies for oil-immersed power transformers

Cited by 18 publications

References 131 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

Comprehensive Evaluation and Scheme Optimization for Power Transmission and Transformation Projects with the Adoption of Carbon Reduction Technologies

Enhancement of Adsorption Performance of Gases in Oil on a Cr₃-Modified SnS₂ Monolayer Based on the First Principles

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State-of-the-art review on asset management methodologies for oil-immersed power transformers

Cited by 18 publications

References 131 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

Comprehensive Evaluation and Scheme Optimization for Power Transmission and Transformation Projects with the Adoption of Carbon Reduction Technologies

Enhancement of Adsorption Performance of Gases in Oil on a Cr3-Modified SnS2 Monolayer Based on the First Principles

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Enhancement of Adsorption Performance of Gases in Oil on a Cr₃-Modified SnS₂ Monolayer Based on the First Principles