EIS aging estimation and threshold values

Trnka, Pavel; Soucek, Jakub; Hornak, Jaroslav; Tozauer, Pavel

doi:10.1109/ichve.2016.7800684

Cited by 3 publications

(2 citation statements)

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“…Models of aging can be divided into various groups as seen in Figure 1. This paper extends, complements and partially corrects the theories presented in Reference [23]. The main aim of this paper is to include a threshold for calculations based on mathematicalphysical presumptions.…”

Section: Introductionsupporting

confidence: 53%

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Proposal of Physical-Statistical Model of Thermal Aging Respecting Threshold Value

Soucek¹,

Trnka

Hornak

2017

Energies

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Abstract:The aging of electrical insulation material or a system is a main issue for designers of high-voltage (HV) machines. Precise determination of the life cycle of electrical insulation is one way of improving the efficiency of electrical machines involved in the production and transmission of electrical energy. Much effort has been devoted to preparing statistical or physical methods of Electrical Insulating System (EIS) life time estimation in the real operation of electrical machinery. The main aim of this paper is to introduce a new physical-statistical model of thermal aging respecting the threshold value. This model is based on thermal aging model and the main difference between this model and previously published models is taking into account the threshold value of degradation factor. The complete design of this model is presented in this paper, including functions defining the threshold value of the effect of the degradation factor depending on the temperature. Proposed model was verified by accelerated thermal aging test at selected temperatures (160, 170, 180 • C) and time intervals (0, 120, 240 h) on a commonly used transformer board. The breakdown voltage was set as an indicating parameter of the level of thermal aging and was measured according to standard IEC 60243-1. Collected data from these measurements were used for threshold value determination (431.23 K) and verification of proposed physical-statistical model of thermal aging respecting the threshold value.

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Section: Introductionsupporting

confidence: 53%

“…The probability density of the proposed probabilistic model is then defined as the first derivative of the distribution function (Equation (20)) based on time and is given in Equation (23).…”

Section: Determining the Threshold Value T T Using A Probabilistic Modelmentioning

confidence: 99%