A new fuzzy information optimization processing technique for monitoring the transformer

In the electricity utilities around the world, a large number of power transformers are operating beyond their design life. The reliability and quality of power transformers is vital to system operation. In order to determine the condition of the insulation in transformers, many methods have been developed. The most interesting methods for identifying fault conditions of the insulation for oil-filled transformers are; dissolved gas analysis (DGA), acoustic analysis for the partial discharge (PD), liquid chromatography, and transfer function techniques. However, these are normally only applied singly to monitor transformers, and thus fail to combine the full information from different methods. This paper establishes a theoretical prototype of a fuzzy information optimization processing technique. It integrates different diagnostic methods and information like DGA, gas rate, acoustic analysis for the PD, transformer temperature, electric current, etc. The method in the paper integrates information theory, fuzzy sets, expert systems. It uses fuzzy mathematics theory to establish the membership function of different information (data) from oil-filled transformers. Other values are obtained directly from expert experience and then integrated by fuzzy matrix theory. A few numerical examples are given