Failure mapping and critical measurements for the operating condition assessment of power transformers

Barkas, Dimitrios; Chronis, Ioannis; Psomopoulos, Constantinos S.

doi:10.1016/j.egyr.2022.07.028

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…Therefore, the analysis and estimation of transformer failure rate is particularly crucial. Accurate analysis of transformer failure rates and prediction of future transformer failure rates can enable power companies to arrange inspection personnel in a timely and reasonable manner, saving a lot of unnecessary inspection costs, and manpower and resources [29]. At the same time, accurate analysis of the transformer failure rate can reasonably plot the distribution of the transformer failure rate over time, which provides a technical basis for introducing life cycle cost management and operating transformers [30].…”

Section: Failure Rate Analysismentioning

confidence: 99%

Life Cycle Cost Estimation and Analysis of Transformers Based on Failure Rate

Zhao,

Gui,

Chen

et al. 2024

Applied Sciences

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Transformers, as the hub equipment of the power system, are highly valued by engineering and scientific researchers in production practice and scientific research. The goal of transformer research is to ensure the safe operation of transformers while considering their economic benefits, arrange transformer inspections reasonably to reduce inspection costs, and save labor and maintenance costs reasonably. This study first provides a brief analysis of the life cycle cost of transformers. The life cycle of transformers is divided into initial cost, operating cost, maintenance cost, fault cost, and scrap cost. Based on the distribution of transformer failure rate, the corresponding life cycle cost of transformers is calculated. Taking 110 kV transformers as an example, the differences in the impact of key factors on the full life cycle cost are analyzed to achieve high quality, high reliability, economically optimal equipment procurement.

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Section: Failure Rate Analysismentioning

confidence: 99%

Life Cycle Cost Estimation and Analysis of Transformers Based on Failure Rate

Zhao,

Gui,

Chen

et al. 2024

Applied Sciences

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“…And to ensure the oil's ability to expel this heat under different operating conditions and over a wide range of temperatures, several characteristics related to viscosity, thermal conductivity, boiling point and evaporation point must be available in oils. The functions of transformer oils are not limited to heat transfer, as they have other roles such as insulation, oxidation prevention and fault detection [4,8].…”

Section: Introductionmentioning

confidence: 99%

Thermal Conductivity Modeling of Dielectric Oils-Based Nanofluids Using the Finite Element Method

Boukounacha,

Zegnini,

Yousfi

et al. 2024

aeletters

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The enhancement of the thermal conductivity of dielectric oils has a positive effect on the performance of electrical equipment that uses these oils as a cooling medium. Nanofluids (NFs) have inspired high-voltage engineers to use them as alternative fluids in power transformers due to their impressive heat transfer and insulation compared to traditional dielectric oils. The present study is a numerical simulation by COMSOL Multiphysics of the thermal conductivity of NFs based on dielectric oils used in power transformers, to identify the effect of temperature, the concentration of nanoparticles (NPs), type of insulating fluid and NPs on thermal conductivity. The NFs were modeled inside a cube using the finite element method (FEM) by applying a temperature gradient. Several types of NPs were used (SiC, ZnO, TiO2, and Al2O3) in addition to several volume concentrations (0%, 0.001%, 0.002%, 0.01%, and 0.02%). The results showed a significant improvement in the thermal conductivity of the NFs with increasing concentration since the best results were recorded at an estimated volume concentration of 0.02%, while the lowest results were obtained for samples using a volume concentration estimated at 0.001%. The base fluid (BF) type and NPs play a dominant role in the thermal performance of the NFs, as the vegetable oil-based nanofluid provided the highest thermal conductivity values and silicon carbides (SiC) was the best NPs used in this study. However, a decrease in thermal transfer capacities was observed for all samples with increasing temperature.

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“…Therefore, data mining and analysis based on actual operating conditions and the construction of low-cost, generalized transformer condition identification and fault early warning models are of great importance to ensure the stable operation of power systems and a quality power supply. The laboratory-based vibration test platform is a common method used by scholars at home and abroad to study transformer fault early warning [7]. In [8], a 110 kV power transformer that was producing GDR II warnings underwent a number of electrical and chemical tests in order to be examined for defects.…”

Section: Introductionmentioning

confidence: 99%

Transformer Fault Warning Based on Spectral Clustering and Decision Tree

Liu¹,

Chen

Li³

et al. 2023

Electronics

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The insufficient amount of sample data and the uneven distribution of the collected data across faults are key factors limiting the application of machine learning in power transformer fault warning, as demonstrated by the poor adaptability of the established data-driven models under actual operating conditions. In this paper, an unsupervised and supervised learning method is designed for power transformer fault early warning based on electrical quantities and vibration signals. The method is based on the Fourier levels of transformer vibration signals under different electrical conditions measured in the field, and the vibration features are clustered according to their intrinsic properties by means of a spectral clustering algorithm. A decision tree model of the vibration characteristics under each cluster is then constructed to calculate early warning values for the transformer vibration spectrum under different electrical conditions, enabling the assessment of transformer production variability. The above process, which is based on field measurement data and data mining analysis methods, is cheaper than the existing transformer fault warning techniques at home and abroad and makes better use of information and training models.

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Failure mapping and critical measurements for the operating condition assessment of power transformers

Cited by 13 publications

References 24 publications

Life Cycle Cost Estimation and Analysis of Transformers Based on Failure Rate

Life Cycle Cost Estimation and Analysis of Transformers Based on Failure Rate

Thermal Conductivity Modeling of Dielectric Oils-Based Nanofluids Using the Finite Element Method

Transformer Fault Warning Based on Spectral Clustering and Decision Tree

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