Spectral analysis for condition assessment of Transformer oil under different aging conditions

Aarathy, A.R.; Pillai, Sakuntala S.; Gopinath, Dinesh; Sajan, S S; Nair, Anjan R; Aryanandiny, B

doi:10.1109/stpec49749.2020.9297709

Cited by 4 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ward et al [18] combined DGA features with partial discharge features to analyze the fault types of power transformers, which overcomes the shortcoming of insufficient individual fault character. Aarathy et al [19] carried out a spectral analysis on the insulating oil of power transformers and established a connection between the spectral change results and the running state, which helps to establish the aging characteristic index of power transformers.…”

Section: Introductionmentioning

confidence: 99%

Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering

Zhu¹,

Dong

2021

IEEE Access

View full text Add to dashboard Cite

The onboard traction transformer is a critical equipment of high-speed trains, its running state directly affects the safety and stability of a train's operation. Given the complexity of the running condition of the onboard traction transformer, this paper proposes a running state diagnosis algorithm based on kernel principal component analysis (KPCA) and fuzzy clustering. To fully extract the status information of the onboard traction transformer, the aging characteristics of insulating oil and main insulation are analyzed under different running mileage as the first step. Thereby, to eliminate the signal redundancy, the status feature set of the onboard traction transformer is analyzed by KPCA combined with the characteristic quantities of the traditional dissolved gas analysis (DGA), and the eigenvalues with the contribution rate of over 95% are used as new eigenvectors. Finally, a status diagnosis model is established by using fuzzy clustering analysis, considering the limitations of fault data of onboard traction transformer. The results from field collected data show that the proposed method is effective in diagnosing the running status of the onboard traction transformer.INDEX TERMS Onboard traction transformer; running status diagnosis; insulation aging; kernel principal component analysis; fuzzy clustering

show abstract

Section: Introductionmentioning

confidence: 99%