A method is proposed for dissolved gases forecast and fault diagnosis in oil-immersed transformers using grey prediction-clustering analysis. Incipient faults can produce hydrocarbon molecules and carbon oxides due to the thermal decomposition of mineral oil, cellulose and other solid insulation. Dissolved gas analysis is employed to detect and monitor abnormal conditions in oil-immersed power transformers. However, the procedure takes a long time to decompose overall key gases and monitor conditions. The grey prediction GM(1, 2) model uses the variant information of hydrogen to forecast the further trends of both combustible and non-combustible gases. Grey clustering analysis is applied to diagnose internal faults including thermal faults, electrical faults and faults involving cellulose degradation. Numerical tests with field gas records were conducted to show the effectiveness of the proposed model, and are easy to implement with the help of portable devices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.