Abstract
This paper presents a new method for calculating the insulation health index (HI) of oil-paper transformers rated under 110 kV to provide a snapshot of health condition using binary logistic regression. Oil breakdown voltage (BDV), total acidity of oil, 2-Furfuraldehyde content, and dissolved gas analysis (DGA) are singled out in this method as the input data for determining HI. A sample of transformers is used to test the proposed method. The results are compared with the results calculated for the same set of transformers using fuzzy logic. The comparison results show that the proposed method is reliable and effective in evaluating transformer health condition.
Highlights
Distribution transformer is one of the most important components in the power grid
This paper presents a new method for calculating the insulation health index (HI) of oil-paper transformers rated under 110 kV to provide a snapshot of health condition using binary logistic regression
Water content in transformer oil, total acidity of oil, dissolved combustible gases (DCG), oil breakdown voltage (BDV), dissipation factor (DF), and 2Furfuraldehyde are selected as the input data, shown in Table 1, for HI calculation
Summary
Distribution transformer is one of the most important components in the power grid. The knowledge about the insulation health condition of a transformer is essential for determining appropriate asset management decisions. It is necessary to identify the most significant measurement results, which clearly represent the health condition of the transformer, in order to improve the HI calculation efficiency. The health status of transformer is categorized as very good, good, moderate, bad, Mathematical Problems in Engineering and very bad in [1] It is classified by EA Technology, a specialist corporation in asset management solutions for owners and operators of electrical asset in UK, as slightly aging, obviously aging, aging beyond the normal range, and extremely poor state. As far as we are concerned, use of binary logistic regression for evaluating the health condition of power transformer has not been published in the literature. Binary logistic regression is preferred to classify the data to get the health status of a sample of transformers rated under 110 kV for the following reasons:.
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