A Fuzzy Evaluation Method of Power Transformer Insulation Health State Based on Multi-weight Fusion

Abstract

During the evaluation of transformer insulation health state, the weight of each evaluation indicator has a significant influence on its evaluation result. To overcome the existing problem of determining the weight of evaluation indicators by a single method, this paper proposes a comprehensive weight fuzzy evaluation method based on multi-weight fusion. Firstly, the proposed method uses principal component analysis (PCA) method to determine the objective weight of each evaluation indicator based on the measured data of the power transformer. Then years of practical experiences from industry experts are introduced to subjectively study the relative importance of the evaluation indicator’s influences on the insulation health states and to build the relative importance of judgment matrix of relative relationship. The subjective weight of each indicator is determined by using the analytic hierarchy process. Next, the objective weight and the subjective weight are fused by using the least square method to obtain the comprehensive weight of the evaluation indicator. And insulation health values and insulation health levels of the power transformers are obtained by using fuzzy comprehensive evaluation. Finally, example analysis and comparison are carried out by using the actual operating data of the transformer. Compared with the single principal component analysis method, the correct rate of the comprehensive weighting method of the evaluation indicator proposed in this paper is improved by 5.9%, which is 10.7% higher than that of the single analytic hierarchy process. The results verify the effectiveness and practicability of the method proposed in this paper. The proposed method provides a scientific basis for the maintenance and overhaul of the transformer. It has a very important practical significance for improving the stability of the power grid and ensuring the normal and orderly progress of the national economy.