Application of Open Agent Architecture and Data Mining Techniques to Transformer Condition Assessment System

The framework of transformer condition assessment system is proposed in this paper through mainly using data warehouse techniques, data mining techniques, and Open Agent Architecture. In this system, a data warehouse is used to collect transformers' testing data, a multi-agent system is used to design the framework of the software, and data mining techniques are used to evaluate transformers' conditions. The present framework is open and flexible, so the objective system is easy to be developed and maintained. The system can support transformers' condition-based maintenance to reduce electric utility's cost. (Because the condition of a transformer depends on its design, present and historical operating data, and relates to its installation environment, load amounts, and so on, it is necessary to integrate all above information to evaluate transformer's condition. However, the off-line testing results, operational data, fault records and weather conditions have been stored in different systems, so finding an effective method to utilize all this information for condition assessment is difficult. Therefore, a data warehouse has been set up to integrate all of the above data, and some data mining techniques have been used to find the pattern and trend of the condition of a transformer. Then whether it is healthy can be determined. In order to make the system open and flexible, Open Agent Architecture (OAA) is employed to compose the multi-agent system. Seven application agents are designed to evaluate transformers' conditions synthetically. The Grey correlation method, grey theory prediction model GM(1,1), Bayesian network classifier and Bayesian network are employed in the agents.