Reinforcement Learning-Based Genetic Algorithm for Aging State Analysis of Insulating Paper at Transformer Hotspot

Jiang, Zaijun; Liu, Jiefeng; Fan, Xianhao; Wang, Qingyin; Zhang, Yiyi; Wu, Thomas

doi:10.1109/tim.2023.3272043

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…When using this approach, hysteresis losses are not taken into account. These losses can be taken into account in models using the semi-empirical formulas of Steinmetz (20) or Bertotti (21) [87] or using complex values of magnetic permeability [88]:…”

Section: Approaches To the Calculation Of Transformer Steelmentioning

confidence: 99%

“…For this reason, the presented review is limited to considering the modeling of the electromagnetic problem and the problem of coupled heat transfer as the main phenomena describing the operation and state of an oil-immersed transformer. This choice can be justified by a significant number of works [6,7,[15][16][17][18][19][20][21][22][23], dedicated to assessing the state of the transformer and aging of insulation, where the temperature distribution and the values of the hottest points are of key importance. As illustrative example, the work [17] is devoted to the study of the degradation of insulating paper under the action of a non-uniform temperature field.…”

Section: Introductionmentioning

confidence: 99%

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Review of Modeling Approaches for Conjugate Heat Transfer Processes in Oil-Immersed Transformers

Smolyanov,

Shmakov,

Butusov

et al. 2024

Computation

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This review addresses the modeling approaches for heat transfer processes in oil-immersed transformer. Electromagnetic, thermal, and hydrodynamic thermal fields are identified as the most critical aspects in describing the state of the transformer. The paper compares the implementation complexity, calculation time, and details of the results for different approaches to creating a mathematical model, such as circuit-based models and finite element and finite volume methods. Examples of successful model implementation are provided, along with the features of oil-immersed transformer modeling. In addition, the review considers the strengths and limitations of the considered models in relation to creating a digital twin of a transformer. The review concludes that it is not feasible to create a universal model that accounts for all the features of physical processes in an oil-immersed transformer, operates in real time for a digital twin, and provides the required accuracy at the same time. The conducted research shows that joint modeling of electromagnetic and thermal processes, reducing the dimensionality of models, provides the most comprehensive solution to the problem.

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Section: Approaches To the Calculation Of Transformer Steelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of Modeling Approaches for Conjugate Heat Transfer Processes in Oil-Immersed Transformers

Smolyanov,

Shmakov,

Butusov

et al. 2024

Computation

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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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Performance Analysis of High-Order RC Circuit Models Applied to Insulation Broadband Impedance Reconstruction in Multi-frequency Applications

Dai,

Bak,

Wang

2024

2024 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)

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Reinforcement Learning-Based Genetic Algorithm for Aging State Analysis of Insulating Paper at Transformer Hotspot

Cited by 5 publications

References 31 publications

Review of Modeling Approaches for Conjugate Heat Transfer Processes in Oil-Immersed Transformers

Review of Modeling Approaches for Conjugate Heat Transfer Processes in Oil-Immersed Transformers

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

Performance Analysis of High-Order RC Circuit Models Applied to Insulation Broadband Impedance Reconstruction in Multi-frequency Applications

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