Association Rule Algorithm-based Automatic Identification of Fault Data in Railway Power Supply and Distribution System
Due to the low identification accuracy of currently prevailing identification methods,an automatic identification method for railway power supply and distribution system fault data based on association rule algorithm is studied.This method first uses wavelet transform to extract fault features in the distribution system,providing important basis for fault identification.It then uses association rule algorithms to mine parameters associated with fault data during which support is used for association rule filtering to optimize the judgment level of association rules,aiming at addressing the problem of excessive and effective association rules.Finally the extracted fault features are compared with the data signals generated by the railway power supply and distribution system to determine whether a fault has occurred.After determining the re-sults,precise positioning of the fault data is achieved based on the mined correlation parameters,thereby realizing auto-matic identification of the fault data of the railway power supply and distribution system.The experimental results show that the proposed method has the highest fault identification accuracy of 96.3,and has a high identification accuracy.It can effectively automatically identify fault data and has good applicative utility.
association rulerailway power supply and distribution systemfaultidentification
万方数据
维普
