The development of smart substations provide technical support for collection and management of big data, and abundant defect data for related analysis about the secondary device. In view of this, this paper establishes the defect data model of the secondary device in smart substations firstly. Secondly, according to characteristics of the defect data model, the Apriori algorithm is improved to reduce the time complexity and memory occupation. Finally, taking defect data of the secondary device of a city in one year as an example, the association rules of the defect data are acquired and analyzed by the improved Apriori algorithm. The result shows that the proposed method can analyze the device defect, search for the weakness of the secondary device, and provide the support for the formulation of defect inspection modes. Besides, compared with the traditional Apriori algorithm, the improved Apriori algorithm reduces the time complexity. This work is supported by National Natural Science Foundation of China (No. 51877084), Natural Science Foundation of Hebei Province (No. E2018502063), and Fundamental Research Funds for the Central Universities (No. 2017MS096).