Frequent Itemsets Mining of SCADA Data Based on FP-Growth Algorithm

When the powergrid fails, a large number of fault data will be transmitted to the dispatching center of SCADA system. Effective and fast processing of these data can minimize the loss of fault and restore the power supply quickly. In this paper, FP growth algorithm is used to mine frequent itemsets, and event folding window is used to discretize the data for easy processing. Through the correlation analysis of actual SCADA data, the results show that the method has a strong practical engineering significance for speeding up the fault analysis after the power grid fault occurs.