Inversion Detection of Transformer Transient Hot Spot Temperature

Ruan, Jiangjun; Deng, Yongqing; Quan, Yu; Gong, Ruohan

doi:10.1109/access.2021.3049235

Cited by 10 publications

(1 citation statement)

References 25 publications

(22 reference statements)

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“…Therefore, the support vector regression machine is used to construct the inversion model of top oil temperature rise. Besides, the K-fold cross-validation training method and grid search (GS) parameters optimization method are used to find the optimal parameters of the SVR model [30,31]. 11.…”

Section: Identification Methods Of Transformer Abnormal Heating Statementioning

confidence: 99%

Inversion detection method of oil‐immersed transformer abnormal heating state

Deng

Ruan

Dong

et al. 2022

IET Electric Power Appl

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Transformer overheating faults have an important impact on the transformer safe and stable operation. Taking 10 kV oil‐immersed transformer as the research object, using the indirect coupling analysis method of electromagnetic thermal fluid multi‐physical field, the transformer heating characteristics under overheating faults, such as winding inter‐turn short circuit and core multi‐point grounding, are analysed, and the transformer abnormal heating state identification method is proposed based on the inversion of transformer top oil temperature rise. By inverting the transformer normal top oil temperature rise under different load rates and comparing it with the actual measured value, taking into account the top oil temperature rise inversion error, it is determined that when the top oil temperature rise measured value is higher than the inversion value by 2.9 K, it indicates the transformer is in an abnormal heating state, and the higher the deviation degree is, the deeper the degree of abnormal heating fault. The accuracy of the transformer abnormal heating state identification method is verified by the multi‐working condition temperature rise test, which can provide a certain reference for the detection and maintenance of transformer abnormal internal heating faults.

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Section: Identification Methods Of Transformer Abnormal Heating Statementioning

confidence: 99%