A Monte-Carlo simulation method for industry transformer health prediction based on dissolved gas analysis

Industry transformer condition monitoring techniques are widely used by the power utilities for condition assessment of oil-paper insulation systems on industry transformers. Among existings monitoring methods, dissolved gas analysis (DGA) is one of the most commonly used techniques in power industry. Various diagnostic models have been developed based on DGA to identify the fault types of industry transformers. However, transformer health prediction is also significant in industry. Therefore, we mainly focus on the time series health prediction of industry transformers based on DGA technique in this paper. Monte-Carlo (MC) simulation is conducted based on DGA method to estimate time series reliability of industry transformers. According to our reliability evaluation, the failure probability of industry transformers will increase with respect to age without proper maintenance.