Research On Insulation Fault Diagnosis Models For Power Transformers Based On Genetic Programming

Power transformer is one of widely distributed, complex and expensive equipment in the power system. It undertakes the heavy task of voltage conversion and power transmission. And its safety state plays a great effect on the stability and security level of power system. It is one of the most important issues for electricity sector to monitor and detect the potential insulation faults of the power transformers. Therefore, it is of great realistic significance to study the fault diagnosis technology and raise the level of maintenance of power transformers. Currently, Dissolved Gases Analysis (DGA) is the most important means to analyze the incipient insulation fault statement of oil-filled power equipments. The principle of DGA is based on the insulation failure mechanism and a lot of actual DGA data to discover the relationship between dissolved gases with the fault reasons and severity of power transformers and conclude the diagnosis rules from it. However, when applying these rules in the practical application, there are still many deficiencies because these rules are mainly generated from the accumulated experience and statistical methods.In recently years, the great development of artificial intelligence technology, such as neural network, fuzzy theory, grey system, rough set and expert system, has provided new research ways to diagnose the faults of power transformers. And it has gradually become the main research method. However, when constructing the diagnosis model based on these traditional artificial intelligence technologies, it usually need to preset the important issues of diagnosis model, such as the model structure, by the experts'knowledge and experience. And the model's structure is usually locally optimized. In a certain extent, it hinders the development and promotion of insulation fault diagnosis system for power transformers. With the development of computer technology and artificial intelligence, allowing computers to discover the inner relationships of system and build the fault diagnosis models automatically has become a hot research issue in the domain of computational intelligence. Genetic programming (GP) algorithm is one of the important branches of evolutionary algorithms in the theory of computational intelligence. GP has achieved wide success in the domain of data mining, control theory, electrical engineering and pattern recognition because of its flexible expression of model structure and global searching ability.In this dissertation, with the characteristics of GP and DGA, four GP-based insulation fault diagnosis models for power transformers have been proposed. The main contents are as...