Aging evaluation for transformer oil-immersed cellulose insulation by using frequency dependent dielectric modulus technique

Liu, Jiefeng; Zhang, Heng; Fan, Xianhao; Zhang, Yiyi; Zhang, Chaohai

doi:10.1007/s10570-020-03592-x

Cited by 24 publications

(11 citation statements)

References 27 publications

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“…In addition, it has been observed from Figures 7 and 8 that a great deal of homocharges are accumulated in the oil-paper under the electrical stresses. This is mainly due to that the number of trapped charges in oil-paper are increased with stressing duration [25][26][27][28][29][30].…”

Section: Temperature(t ) 293 Kmentioning

confidence: 99%

Description of space charge transport in oil‐paper insulation using adaptive time‐stepping transient upstream finite element method

et al. 2021

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The charge transport and accumulation in oil-paper can cause the insulation degradation. So far, the most widely used model to simulate space charge transport and accumulation is the bipolar charge transport (BCT) model, which can well simulate the space charge dynamics. However, there are two shortcomings in the algorithms for solving the BCT model. One is that there is almost no use of vectorisation technology, which may increase the complexity of the algorithm, the other is the usage of fixed step size which might bring extra computation cost. In view of this, an adaptive time-stepping transient upstream finite element method (FEM) is developed to solve the BCT model considering trapping/detrapping, as well as the recombination phenomenon under DC condition in this article. Then, a vectorisation technology is used in the method to optimise the algorithm. Moreover, the adaptive time-stepping method is introduced in simulation to reduce computation time and calculation amount. Simulation results are obtained by programing and later presented, which are basically consistent with the corresponding experimental results. Therefore, the proposed method is expected to promote the optimization design of the oil-paper insulation system.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Temperature(t ) 293 Kmentioning

confidence: 99%

Description of space charge transport in oil‐paper insulation using adaptive time‐stepping transient upstream finite element method

et al. 2021

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“…Through the above analysis of the traction transformer loss, the simulation model's load data are obtained. The core loss data is calculated by equation (2), and the windings loss data is calculated by equation ( 3) and equation (7). The calculation results are presented in Table 2.…”

Section: ) Loss Analysis Under Step Loadmentioning

confidence: 99%

“…As an essential part of urban rail transit, the traction transformer operation's reliability depends on its insulation performance [1], [2]. The traction transformer's insulation performance is closely related to the heat generation and heat dissipation inside the traction transformer [3], [4].…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model of Hot Spot Temperature for Split-Windings Traction Transformer Considering the Load Characteristics

et al. 2021

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Hot spot temperature (HST) is an important parameter reflecting the working state of the traction transformer, and the load characteristics have a great influence on the HST. The current researches rarely consider the load characteristics of transformers in-depth, and most of the research on the temperature field focuses on steady load. Therefore, a prediction model of HST for the traction transformer considering the load characteristics is established by the finite element simulation in this paper. Considering the impact and nonlinearity of traction load, the influence of the load characteristics on the HST is studied. Analysis of the load curve revealed that the steady and step load combination could well simulate the traction load. Based on the analysis of traction transformer load characteristics, the temperature field coupling calculation of traction transformer is carried out according to the VI normative duty class. On this basis, the influence of load rate and time on HST is studied. The present findings show that the method can predict the HST of traction transformer under different working conditions and provide a reference for load dispatching of traction transformer. The simulation data are in good agreement with the test results, and the average relative error is 3.4%. The results verify the effectiveness of the proposed method.INDEX TERMS Traction transformers, hot spot temperature, load characteristics, temperature field analysis, finite element simulation.

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“…The power transformer is an indispensable piece of equipment in power grids, as it is responsible for voltage-level transformation, and its operational status is related to the safe and stable operation of the power grid [ 1 , 2 , 3 , 4 ]. The insulating materials inside the transformer are composed of oil and paper/pressboard.…”

Section: Introductionmentioning

confidence: 99%

Concentration Prediction of Polymer Insulation Aging Indicator-Alcohols in Oil Based on Genetic Algorithm-Optimized Support Vector Machines

Zhang

Wang

et al. 2022

Polymers

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The predictive model of aging indicator based on intelligent algorithms has become an auxiliary method for the aging condition of transformer polymer insulation. However, most of the current research on the concentration prediction of aging products focuses on dissolved gases in oil, and the concentration prediction of alcohols in oil is ignored. As new types of aging indicators, alcohols (methanol, ethanol) are becoming prevalent in the aging evaluation of transformer polymer insulation. To address this, this study proposes a prediction model for the concentration of alcohols based on a genetic-algorithm-optimized support vector machine (GA-SVM). Firstly, accelerated thermal aging experiments on oil-paper insulation are conducted, and the concentration of alcohols is measured. Then, the data of the past 4 days of aging are used as the input feature of SVM, and the GA algorithm is utilized to optimize the kernel function parameter and penalty factor of SVM. Moreover, the concentrations of methanol and ethanol are predicted, after which the prediction accuracy of other algorithms and GA-SVM are compared. Finally, an industrial software program for predicting the concentration of methanol and ethanol is established. The results show that the mean square errors (MSE) of methanol and ethanol concentration predictions of the model proposed in this paper are 0.008 and 0.003, respectively. The prediction model proposed in this paper can track changes in methanol and ethanol concentrations well, providing a theoretical basis for the field of alcohol concentration prediction in transformer oil.

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Aging evaluation for transformer oil-immersed cellulose insulation by using frequency dependent dielectric modulus technique

Cited by 24 publications

References 27 publications

Description of space charge transport in oil‐paper insulation using adaptive time‐stepping transient upstream finite element method

Description of space charge transport in oil‐paper insulation using adaptive time‐stepping transient upstream finite element method

A Prediction Model of Hot Spot Temperature for Split-Windings Traction Transformer Considering the Load Characteristics

Concentration Prediction of Polymer Insulation Aging Indicator-Alcohols in Oil Based on Genetic Algorithm-Optimized Support Vector Machines

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