Review of condition assessment of power transformers in service

Wang, May D.; Vandermaar, A.J.; Srivastava, K. D.

doi:10.1109/mei.2002.1161455

Cited by 785 publications

(329 citation statements)

References 31 publications

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“…Unfortunately, during their long operation period, the ageing of transformer insulation inevitably occurs due to the complicated operating conditions [3][4][5]. Once the power transformer ages, its insulation compounds obviously degrade, which increases risk of dielectric failure.…”

Section: Introductionmentioning

confidence: 99%

Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model

et al. 2017

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Polarization-depolarization current (PDC) measurements are now being used as a diagnosis tool to predict the ageing condition of transformer oil-paper insulation. Unfortunately, it is somewhat difficult to obtain the ageing condition of transformer cellulose insulation using the PDC technique due to the variation in transformer insulation geometry. In this literature, to quantify the ageing condition of transformer cellulose insulation using the PDC technique, we firstly designed a series of experiments under controlled laboratory conditions, and then obtained the branch parameters of an extended Debye model using the technique of curve fitting the PDC data. Finally, the ageing effect and water effect on the parameters of large time constant branches were systematically investigated. In the present paper, it is observed that there is a good exponential correlation between large time constants and degree of polymerization (DP). Therefore, the authors believe that the large time constants may be regard as a sensitive ageing indicator and the nice correlations might be utilized for the quantitative assessment of ageing condition in transformer cellulose insulation in the future due to the geometry independence of large time constants. In addition, it is found that the water in cellulose pressboards has a predominant effect on large time constants.

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Section: Introductionmentioning

confidence: 99%

Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model

et al. 2017

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“…This strategy is based on the repair measures that are carried out on the actual condition of electrical equipment [1]. This requires reliable and timely information on the technical condition of electrical equipment such as power transformers.…”

Section: Introductionmentioning

confidence: 99%

Power Transformers Diagnostics using Heat Model Parameter Identification

Ivan¹,

Artem²

2017

International Journal of Innovative Research in Electrical, Ele

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Condition evaluation of the electrical equipment in operation is an important prerequisite for building reliable and efficient electrical systems and networks. In order to increase accuracy of power transformers condition monitoring, method for analyzing the diagnostic parameters of power transformer oil temperature have been developed. The method is based on parametric identification of the thermal model using particle swarm optimization. In order to test the efficiency of the proposed method, simulation was performed using the experimental data obtained on the power three-winding transformer of the network substation. In view of the impossibility of deliberately changing the technical condition of the transformer, the performance evaluation is based on the confirmation of the absence of changes in the technical state. The analysis of the obtained results confirms the efficiency of the developed method and algorithms.

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“…This technique found wide acceptance in production plants by the late 1990s [2]. In 2002, a review for condition assessment of power transformer was conducted in [3] that concluded an increasing need for diagnostic and monitoring tools. The further development of diagnostic measurements such as dissolved gas analysis (DGA), frequency response analysis (FRA), etc.…”

Section: Introductionmentioning

confidence: 99%

Individual failure rates for transformers within a population based on diagnostic measures

Jürgensen

Nordström

Hilber

2016

Electric Power Systems Research

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This is the published version of a paper published in Electric power systems research. Citation for the original published paper (version of record):Jürgensen, J H., Nordström, L., Hilber, P. (2016) Individual failure rates for transformers within a population based on diagnostic measures. The high monetary value of a transformer has placed the transformer life-time optimization into the focus of asset management. The average failure rate has created reasonable results within reliability modeling, however, it cannot reflect the probability of failure for an individual transformer. In this paper, a method is introduced to calculate individual failure rates for a transformer population based on failure statistics and diagnostic measurements such as dissolved gas, and 2-furfuraldehyde analysis. The method is applicable to all types of components and the comprehensibility makes it effective for practical implementation. The results are evaluated against two health indices based on a weight factor and fuzzy logic. It can be observed that the presented individual failure rates are plausible representatives of the transformer's probability of failure. Therefore, the results can also be utilized for asset management decision-making.

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Review of condition assessment of power transformers in service

Cited by 785 publications

References 31 publications

Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model

Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model

Power Transformers Diagnostics using Heat Model Parameter Identification

Individual failure rates for transformers within a population based on diagnostic measures

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