Fault prediction of Power Transformer by Association Rules and Markov

At present, most transformer fault prediction methods mainly focus on the prediction of state parameters, and the accuracy of the prediction results depends heavily on the quality of the data that is used by them. Aiming at solving these problems, a fault prediction method of power transformer by association rules and Markov is proposed. Firstly, apriori algorithm is applied for mining the association rules between different states to establish the state transition matrix of transformer states. Then a multi-dimensional correction factor system including family defects, operating environment and overhauling records is established and the relative degree of degradation is introduced to calculate them. Besides, an array-based apriori algorithm is proposed to realize the weight of correction factors by combining the mining rules of correction factors and states. Finally, the state of the transformer is predicted with the existing transformer state probability as the initial vector and the modified state transition matrix. The results reveal that the established prediction model can predict the development trend of transformer faults effectively and the modified transition probability matrix can make full use of various state information of power transformers so as to improve the effect of transformer fault prediction.