Transformer Fault Diagnosis Based on BP Neural Network by Improved Apriori Algorithm

Guoxiang, Chang; Qiaoli, Gao; Xinming, Gao; Jun-ting, Cheng

doi:10.1007/978-3-030-62483-5_27

Cited by 1 publication

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“…Their presence is usually not too dangerous and can be used to analyze the degree of oxidation of insulation oil. Monitoring and analyzing dissolved gases in transformer oil is a routine maintenance practice [2]. By regularly sampling and analyzing oil samples, potential problems can be detected and appropriate repair and maintenance measures can be taken to ensure the reliability and safety of transformers.…”

Section: Introductionmentioning

confidence: 99%

Research on transformer fault intelligent diagnosis technology based on improved random forest algorithm

Zhou,

Xu,

Yuan

et al. 2024

J. Phys.: Conf. Ser.

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Transformer oil can dissolve a certain amount of gas, which can provide an important basis for transformer fault diagnosis. The relationship between the characteristic amount of dissolved gas in transformers and the type of transformer fault was analyzed, and the main influencing factors were extracted. We studied a transformer fault diagnosis method based on the improved random forest algorithm and combined it with the identification requirements of abnormal data in multiple parts of transformers. We improved the random forest from four aspects: dataset construction, bootstrap sampling, decision tree generation, and multi-node voting. We established an improved random forest diagnosis model with oil chromatogram feature gas ratio as the characteristic parameter and analyzed different input parameter combinations. The effectiveness of improving the random forest diagnosis model under different training times was compared with various machine learning methods. The experimental results show that the diagnostic accuracy of the improved random forest diagnostic model is significantly improved compared to other machine learning methods.

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Section: Introductionmentioning

confidence: 99%