Application of ensemble classification method for power transformers condition assessment

The increase in the amount of data acquired from the monitoring of power system components has motivated utilities to employ effective strategies for processing the information collected. Hence, salient features can be identified and efficient decisions is made. An important component of any power system is power transformers, which have the single highest value of the equipment installed in high-voltage substations. For this reason, significant attention has been devoted to transformer monitoring and diagnostic techniques, resulting in huge volumes of raw data, especially related to the detection of any abnormal transformer behavior. The application of many monitoring tests is therefore not always useful, creating a critical need for a rational method of minimizing the number of monitoring tests without losing essential information about the actual condition of the transformer. This paper presents a statistical approach for evaluating the state of the transformer using machine learning technique. Demonstration of the use of classifier ensemble to predict transformer condition was also made.