Journal
IEEE SENSORS JOURNAL
Volume 22, Issue 21, Pages 21145-21152Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2022.3204844
Keywords
Current measurement; Sensors; Power transformer insulation; Reliability; Time measurement; Temperature measurement; Ammeters; Activation energy; dielectric measurement; oil-paper insulation; power transformer; rise time
Funding
- Department of Science and Technology (DST) through the Science and Engineering Research Board (SERB) [CRG/2018/001374]
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This study proposes a noninvasive and effective method to predict the activation energy of insulation materials by recording the dielectric response. The method requires a few seconds of polarization current data and can quickly sense the value of activation energy with high accuracy.
Activation energy represents the average rate of interaction between aging by-products and cellulose. Activation energy is a crucial parameter that can be used to identify the remaining life of insulation in high voltage (HV) equipment. Existing noninvasive methods take a significantly longer time to sense activation energy for given insulation. This is primarily due to the volume of data required for such analysis, which generally takes significant time to measure. This work reports a noninvasive and effective approach to predict activation energy of oil-paper insulation using a dielectric response that is recorded for a very short span of time. The proposed method requires polarization current data sensed for a few seconds (15-20 s) to operate. The initial decay rate (DR) of the sensed data is found to be sensitive to the activation energy. This feature of the initial DR is utilized to sense the value of activation energy within a short duration. The proposed technique utilizes the current sensor (present within an electrometer) more efficiently. This facilitates the measurement of a highly accurate polarization profile and ensures reliable activation energy estimation. The proposed methodology has been successfully applied to data collected from a few real-life transformers. Reported results show that the suggested method provides satisfactory results with good accuracy.
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