Application of fuzzy logic and evidential reasoning methodologies in transformer insulation stress assessment

Transformer life and age is taken to be that of its insulation system. In order to facilitate decision making on transformer optimization in maintenance, loading, relocation and replacement, the insulation system condition must be monitored and evaluated based on faults and aging stress levels. Various techniques can be applied to this effect. This paper presents the application of two such techniques: fuzzy logic and evidential reasoning. Over twenty case studies are evaluated using real field data with the same inputs given to the two models. The data is looked at from different transformer perspectives like dissolved gases, insulation physical, chemical and electrical properties but finally categorized into fault stresses and aging stresses. These transformer stresses are analyzed from various measurable variables that characterizes them. Logical transformer management decisions can be taken based on the stress level assessment outcome. Trapezoidal, generalized bell-shaped and Gaussian membership functions were applied to assess their effect on the fuzzy logic outcome. The results show evidential reasoning to be better in terms of flexibility and ability to handle measurement uncertainty whereas fuzzy logic is advantageous with a large volume of expert database.