Stray Loss Evaluation of Power Transformers Using Simplified Air-core Model with Tank and Frame

Liu, Botao; Takahashi, Yasuhito; Fujiwara, Koji; Imamori, Satoshi

doi:10.1109/intermagshortpapers58606.2023.10228440

2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers) 2023

DOI: 10.1109/intermagshortpapers58606.2023.10228440

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Stray Loss Evaluation of Power Transformers Using Simplified Air-core Model with Tank and Frame

Botao Liu,

Yasuhito Takahashi,

Koji Fujiwara

et al.

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2024

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Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Ngwenyama,

Gitau

2024

Sci Rep

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Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.

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Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Ngwenyama,

Gitau

2024

Sci Rep

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Stray Loss Evaluation of Power Transformers Using Simplified Air-core Model with Tank and Frame

Cited by 1 publication

References 5 publications

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

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