Monitoring HV transformer conditions: The strength of combining various diagnostic property observations

Montanari, Gian Carlo; Morshuis, P.H.F.; Cervi, A.

doi:10.1109/icacact.2014.7223590

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Based on the analysis, recommendations can be made to carry out the necessary mitigation actions [13]. Other techniques that are commonly used to evaluate faults in transformers include frequency response, partial discharge and recovery voltage methods [14,15]. As part of transformers management practices, external conditions of transformers are also monitored that include information, such as oil leak as well as the state of the oil seals, foundations, and tanks [16,17].…”

Section: Introductionmentioning

confidence: 99%

Transformers Health Index Assessment Based on Neural-Fuzzy Network

et al. 2018

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In this paper, an assessment on the health index (HI) of transformers is carried out based on Neural-Fuzzy (NF) method. In-service condition assessment data, such as dissolved gases, furans, AC breakdown voltage (ACBDV), moisture, acidity, dissipation factor (DF), color, interfacial tension (IFT), and age were fed as input parameters to the NF network. The NF network were trained individually based on two sets of data, known as in-service condition assessment and Monte Carlo Simulation (MCS) data. HI was also obtained from the scoring method for comparison with the NF method. It is found that the HI of transformers that was obtained by NF trained by MCS method is closer to scoring method than NF trained by in-service condition assessment method. Based on the total of 15 testing transformers, NF trained by MCS data method gives 10 transformers with the same assessments as scoring method as compared to eight transformers given by NF trained by in-service condition data method. Analysis based on all 73 transformers reveals that 62% of transformers have the same assessments between NF trained by MCS data and scoring methods.

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