Transformer Health Index Research Articles

Absolute and Relative Scaling of Power Transformers using Grey Incidence and Relational Analysis

ealth or criticality index of power transformer is computed by utilizing the heuristic knowledge, employed on massive parametric data. For assessment purpose, it is unreasonable to collect the huge data of monitoring and test equipments frequently. As a first information source, dissolved gases-in- oil is secured in interpreting the transformer condition. The major seven dissolved gases are referred as key gases in IEEE/IEC gas guide. Dissolved Combustible Gases (TDCG) standard for state assessment uses caution levels of key gases for condition judgment. The phenomenon of dissolved-gas-in- oil observed analogous to grey system, as one can use this partial information about transformer for health index determination. Grey system theory deals with the incomplete information in system analysis. The objective of this paper is, to evaluate the health index of transformers by means of Grey Incidence Analysis (GIA) and Grey Relational Analysis (GRA) for absolute and relative scaling respectively. Synthetic degree of GIA is employed for absolute scaling, where the test samples are compared with industry standard. However, test samples are compared without applying any standard model in GRA by means of approaching degree. The assessment results from grey analysis are further examined by Adaptive Neuro-Fuzzy Inference System (ANFIS) and Network Fitting (NF) tool. The proposed measurement is promising in priority based maintenance activities of power transformer.

Calculation of a Health Index of Oil-Paper Transformers Insulation with Binary Logistic Regression

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.

Power Transformers Health Index Calculation Method Based on Cloud Model and Fuzzy Evidential Reasoning

Transformers health index calculation method based on cloud model and fuzzy evidential reasoning is proposed. According to the multi-level and multifactor of evaluation index information of power transformers, a layered evaluation index model is established. In order to deal with the ambiguity and uncertainty information of evaluation index, a normal cloud model is introduced, inferred the fuzzy degree of belief in the health state of evaluation index. Then use the fuzzy evidential reasoning method merge information of evaluation Index, inferred the degree of belief in the health state-level of transformer, calculated the health index of transformer. The results of an example analysis test its rationality and effectiveness.