Substation Operation Information Maintenance Based on Intelligent Data Mining

Abstract

In order to solve the problems of redundancy of substation operation information, diversification of monitoring system versions, and heterogeneous data, the author proposes a remote-based intelligent data mining system for substation automation. Use data mining technology to classify and standardize the original alarm data, and associate it with the equipment ledger system to obtain a data warehouse of equipment parameters, status information, and related historical fault information. On this basis, the improved Apriori algorithm is used to extract strong association rules that satisfy the minimum trust threshold from the data warehouse, which provides decision-making basis for the operation and maintenance management of substation equipment. Experimental results show that using the improved Apriori algorithm to perform data mining on the substation operation alarm information in 143 intervals of a power supply bureau substation, the following strong association rules are obtained: association rule 1, “handcart test position ∩ device running alarm ∩ alarm signal has been reset interval debugging and maintenance”, γ support = 57 % , γ confidence = 69 % , and association rule 2, “Protection action∩ switch sectional position ∩ reclosing action ∩ total accident (hold) ∩ switch sectional signal has been reset ∩ reclosing action signal has been reset ∩ total accident (hold) signal has been reset Temporary equipment failure”, γ support = 41 % , γ confidence = 79 % . The effectiveness of the proposed data mining method is verified by a field case.