Generation of synthetic datasets for transformer’s dissolved gas analysis using Monte-Carlo Simulation

Abstract

The fault diagnosis in power transformers is carried out using Dissolved Gas Analysis (DGA). Although DGA does provide key information for fault detection, the method is inherently complex. Several methods have been developed for DGA, but still possess challenges in accurately detecting the fault. A method has been developed to generate synthetic data using Monte-Carlo simulation. The generated synthetic data is feed into DGA excel tool to investigate the accuracy of fault detection. The synthetic data can be used to further enhance the DGA tool, improve its accuracy and investigate the inclusive faults. A model has been proposed for the integration of synthetic data generator with DGA tool for machine learning and to obtain an automated and improved DGA tool for fault diagnoses in power transformers.