In order to forecast the development of power transformer fault effectively

we propose a Markov model to forecast the fault of power transformer which is based on association rules analysis.Firstly

the cloud concept of transformer state parameter is extracted by the cloud theory

and the Apriori algorithm is applied to mining the association rules between the transformer state parameter and the states of power transformer and the association rules between different states.Secondly

according to the association rules between the state parameter and the states

an analysis model of transformer state based on a cloud-petri network is established

which can get the possibility of each initial state.Thirdly

the state transition matrix of transformer states based on the association rules between different states is built.And a correction factor system is established

which can correct the state transition matrix.Finally

this Markov model uses the possibilities of the initial states of power transformer to forecast the fault with the corrected state transition matrix.The test result reveals that

compared with IEC

BPNN

SVM

the analysis model based on could-petri network has faster response time or higher accuracy.And the matrix correcting can improve the accuracy of Markov model prediction

which can forecast the power transformer fault trend more effectively and reasonably.